From 5c136d583a4bbc36bb1eeb8d2eab990e0f7b31de Mon Sep 17 00:00:00 2001 From: Badr Ouali <32390048+oualib@users.noreply.github.com> Date: Wed, 30 Oct 2024 04:55:25 -0400 Subject: [PATCH] [V1.1.0] Iteration 3 (#1329) * [V1.1.0] Final before release. - We are making the final adjustments and then we will release the v1.1.0. * correcting kprototype example * Update __init__.py * Correcting Fortify issues SHould close: https://jira.verticacorp.com/jira/browse/VER-97398?filter=-1 * adding minimum version to QPROF * corrections multiple corrections * other changes * del image files * added pngs * fixed images in about us * fixed doc versions * Update examples.rst * Delete user_guide_performance_qprof_get_qplan_tree.PNG * Create user_guide_performance_qprof_get_qplan_tree.png * Update getting_started.rst fixed website uRL * Update qprof.py removed unnecessary note * Update user_guide_performance_qprof.rst * Update user_guide_introduction_vdf.rst * corrections * Update user_guide_introduction_vdf.rst * changing the wrong dir * added custom help function for shorter doc string view * updated whatsnew * Update qprof_interface.py --------- Co-authored-by: Umar Farooq Ghumman --- ...coverage_1.PNG => img_code_coverage_1.png} | Bin ...coverage_2.PNG => img_code_coverage_2.png} | Bin ...coverage_3.PNG => img_code_coverage_3.png} | Bin .../{testi-author2.png => testi-author2.jpg} | Bin ...uide_performance_qprof_get_qplan_tree.png} | Bin .../whats_new_1_1_0_qprof_get_qplan_tree.png | Bin 0 -> 1399910 bytes docs/source/about_us.rst | 44 ++--- docs/source/api.rst | 2 +- docs/source/cicd_codecov.rst | 6 +- docs/source/contribution_guidelines.rst | 2 +- docs/source/examples.rst | 2 +- docs/source/examples_business.rst | 19 ++ .../source/examples_business_base_station.rst | 6 +- docs/source/examples_business_battery.rst | 2 +- docs/source/examples_business_booking.rst | 2 +- docs/source/examples_business_churn.rst | 2 +- .../examples_business_credit_card_fraud.rst | 26 +-- docs/source/examples_business_football.rst | 8 +- docs/source/examples_business_movies.rst | 8 +- .../source/examples_business_smart_meters.rst | 18 +- docs/source/examples_business_spotify.rst | 14 +- docs/source/examples_learn_commodities.rst | 2 +- docs/source/examples_learn_iris.rst | 2 +- docs/source/examples_learn_pokemon.rst | 4 +- docs/source/examples_learn_titanic.rst | 4 +- docs/source/examples_learn_winequality.rst | 2 +- docs/source/examples_understand.rst | 7 +- .../examples_understand_africa_education.rst | 4 +- docs/source/examples_understand_amazon.rst | 2 +- docs/source/examples_understand_covid19.rst | 2 +- docs/source/getting_started.rst | 2 +- docs/source/performance_vertica.rst | 12 +- .../user_guide_data_exploration_charts.rst | 2 +- ...ata_exploration_descriptive_statistics.rst | 19 +- docs/source/user_guide_data_ingestion.rst | 18 +- docs/source/user_guide_data_preparation.rst | 6 +- .../user_guide_data_preparation_joins.rst | 2 +- .../user_guide_data_preparation_outliers.rst | 6 +- ... => user_guide_data_preparation_scale.rst} | 38 ++-- ...ser_guide_full_stack_complex_data_vmap.rst | 24 +-- ...er_guide_full_stack_dblink_integration.rst | 129 ++++++------- ...ser_guide_full_stack_linear_regression.rst | 16 +- docs/source/user_guide_full_stack_udf.rst | 2 +- ...user_guide_full_stack_vdataframe_magic.rst | 4 +- ...user_guide_introduction_best_practices.rst | 18 +- .../user_guide_introduction_installation.rst | 25 ++- docs/source/user_guide_introduction_vdf.rst | 171 +++++++----------- ..._guide_machine_learning_classification.rst | 4 +- ...user_guide_machine_learning_clustering.rst | 4 +- ...er_guide_machine_learning_introduction.rst | 2 +- ..._guide_machine_learning_model_tracking.rst | 21 ++- ...user_guide_machine_learning_regression.rst | 4 +- ...ser_guide_machine_learning_time_series.rst | 2 +- docs/source/user_guide_performance_qprof.rst | 10 +- docs/source/vertica_documentation.rst | 2 +- docs/source/whats_new_v1_1_0.rst | 45 ++++- verticapy/__init__.py | 6 +- verticapy/_utils/_sql/_vertica_version.py | 4 + verticapy/connection/utils.py | 4 +- verticapy/core/vdataframe/_corr.py | 52 +++--- verticapy/core/vdataframe/_filter.py | 8 +- .../core/vdataframe/_machine_learning.py | 4 +- verticapy/core/vdataframe/_math.py | 4 +- verticapy/core/vdataframe/_rolling.py | 4 +- .../model_selection/model_validation.py | 4 +- verticapy/machine_learning/vertica/cluster.py | 4 +- .../machine_learning/vertica/ensemble.py | 4 +- .../machine_learning/vertica/tsa/ensemble.py | 6 +- verticapy/performance/vertica/qprof.py | 14 +- .../performance/vertica/qprof_interface.py | 52 ++++-- .../_highcharts/machine_learning/svm.py | 10 +- .../_matplotlib/machine_learning/svm.py | 7 +- verticapy/plotting/base.py | 17 +- 73 files changed, 521 insertions(+), 461 deletions(-) rename docs/source/_static/{img_code_coverage_1.PNG => img_code_coverage_1.png} 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diff --git a/docs/source/cicd_codecov.rst b/docs/source/cicd_codecov.rst index f5ef1b796..71015870f 100644 --- a/docs/source/cicd_codecov.rst +++ b/docs/source/cicd_codecov.rst @@ -21,7 +21,7 @@ For the latest statistics on code-coverage of ```verticapy``` please look at `this link `_. -.. image:: /_static/img_code_coverage_1.PNG +.. image:: /_static/img_code_coverage_1.png :width: 50% :align: center @@ -31,7 +31,7 @@ Not only does it provide a wholistic view of the contributors and in-depth understanding of which folders/functions are lacking behind the most. -.. image:: /_static/img_code_coverage_2.PNG +.. image:: /_static/img_code_coverage_2.png In the details, we can see the total number of lines as well as the number of lines in each folder that have @@ -42,7 +42,7 @@ at the exact lines that are missing unit tests. This can guide contributors in understanding the gaps in testing. -.. image:: /_static/img_code_coverage_3.PNG +.. image:: /_static/img_code_coverage_3.png In summary, developers use code coverage tools to gain insights into which parts of their code are well-tested diff --git a/docs/source/contribution_guidelines.rst b/docs/source/contribution_guidelines.rst index b9dbb0298..43488c660 100644 --- a/docs/source/contribution_guidelines.rst +++ b/docs/source/contribution_guidelines.rst @@ -19,7 +19,7 @@ Contribution Guidelines VerticaPy
- Documentation 1.0.x + Documentation 1.1.x
diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 408f6edc7..0c32ea752 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -48,7 +48,7 @@ Examples :class-card: custom-card-examples :class-footer: user_guide_footer - 10 Nmins + 10 Notebooks ^^^^^^^^^^^^^^ Solve real-world problems with VerticaPy. diff --git a/docs/source/examples_business.rst b/docs/source/examples_business.rst index 89518c066..9ee3a5660 100644 --- a/docs/source/examples_business.rst +++ b/docs/source/examples_business.rst @@ -23,6 +23,7 @@ Business optimizing base station operations. Simplify your exploration of base station efficiency predictions with our user-friendly approach. + .. grid-item:: .. card:: Battery :img-top: _static/website/examples/images/battery.jpg @@ -39,6 +40,8 @@ Business overall health. Simplify your exploration of battery health predictions with our user-friendly approach. + .. grid-item:: + .. card:: Booking :img-top: _static/website/examples/images/booking.jpg :link: examples.business.booking @@ -54,6 +57,8 @@ Business into optimizing booking strategies. Simplify your exploration of flight booking predictions with our user-friendly approach. + .. grid-item:: + .. card:: Churn :img-top: _static/website/examples/images/churn.jpg :link: examples.business.churn @@ -69,6 +74,8 @@ Business retaining subscribers. Simplify your exploration of Telco churn predictions with our user-friendly approach. + .. grid-item:: + .. card:: Credit Card Fraud :img-top: _static/website/examples/images/credit_card_fraud.jpg :link: examples.business.credit_card_fraud @@ -86,6 +93,8 @@ Business Simplify your exploration of credit card fraud predictions with our user-friendly approach. + .. grid-item:: + .. card:: Football :img-top: _static/website/examples/images/football.jpg :link: examples.business.football @@ -102,6 +111,8 @@ Business match results. Simplify your exploration of football outcome predictions with our user-friendly approach. + .. grid-item:: + .. card:: Insurance :img-top: _static/website/examples/images/insurance.jpg :link: examples.business.insurance @@ -118,6 +129,8 @@ Business Simplify your exploration of health insurance predictions with our user-friendly approach. + .. grid-item:: + .. card:: Movies :img-top: _static/website/examples/images/movies.jpg :link: examples.business.movies @@ -134,6 +147,8 @@ Business into the potential success of films. Simplify your exploration of movie rating predictions with our user-friendly approach. + .. grid-item:: + .. card:: Smart meters :img-top: _static/website/examples/images/smart_meters.jpg :link: examples.business.smart_meters @@ -150,6 +165,8 @@ Business into optimizing resource efficiency. Simplify your exploration of smart meter consumption predictions with our user-friendly approach. + .. grid-item:: + .. card:: Spam :img-top: _static/website/examples/images/spam.jpg :link: examples.business.spam @@ -166,6 +183,8 @@ Business enhancing your inbox security. Simplify your exploration of spam detection predictions with our user-friendly approach. + .. grid-item:: + .. card:: Spotify :img-top: _static/website/examples/images/spotify.jpg :link: examples.business.spotify diff --git a/docs/source/examples_business_base_station.rst b/docs/source/examples_business_base_station.rst index fa92e53b6..daa9e732b 100644 --- a/docs/source/examples_business_base_station.rst +++ b/docs/source/examples_business_base_station.rst @@ -40,7 +40,7 @@ This example uses the following version of VerticaPy: vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. @@ -402,7 +402,7 @@ Data Preparation Finding Clusters of Base Stations ++++++++++++++++++++++++++++++++++ -We create virtual base stations by grouping existing base stations in 100 clusters. Clustering is performed using ``k-means`` clustering on Euclidean coordinates of the base stations. Each cluster represents a wider coverage of connections. +We create virtual base stations by grouping existing base stations in 100 clusters. Clustering is performed using :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` clustering on Euclidean coordinates of the base stations. Each cluster represents a wider coverage of connections. .. ipython:: python @@ -725,7 +725,7 @@ Let's find a suitable number of clusters using elbow curve. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_base_station_elbow_longi_lati.html -The :py:func:`~verticapy.machine_learning.model_selection.elbow` curve seems to indicate that 4 would be a good number of clusters, so let's try k = 4 and view the weighted ``k-means`` algorithm's suggested positions for new base stations based on the centers of the clusters. +The :py:func:`~verticapy.machine_learning.model_selection.elbow` curve seems to indicate that 4 would be a good number of clusters, so let's try k = 4 and view the weighted :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` algorithm's suggested positions for new base stations based on the centers of the clusters. .. ipython:: python :okwarning: diff --git a/docs/source/examples_business_battery.rst b/docs/source/examples_business_battery.rst index 4736e9ed8..b5c98ec85 100644 --- a/docs/source/examples_business_battery.rst +++ b/docs/source/examples_business_battery.rst @@ -48,7 +48,7 @@ This example uses the following version of VerticaPy: import verticapy as vp vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. diff --git a/docs/source/examples_business_booking.rst b/docs/source/examples_business_booking.rst index 958ea12a7..300c782d2 100644 --- a/docs/source/examples_business_booking.rst +++ b/docs/source/examples_business_booking.rst @@ -41,7 +41,7 @@ This example uses the following version of VerticaPy: vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. diff --git a/docs/source/examples_business_churn.rst b/docs/source/examples_business_churn.rst index 48ff6188b..f5816d081 100644 --- a/docs/source/examples_business_churn.rst +++ b/docs/source/examples_business_churn.rst @@ -354,7 +354,7 @@ We'll use a lift chart to help us identify which of our customers are likely to .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_churn_lift_chart.html -By targeting less than 30% of the entire distribution, our predictions will be more than three times more accurate than the other 70%. +By targeting less than ``30%`` of the entire distribution, our predictions will be more than three times more accurate than the other ``70%``. Conclusion ----------- diff --git a/docs/source/examples_business_credit_card_fraud.rst b/docs/source/examples_business_credit_card_fraud.rst index 5029b2b86..a6fe2dfa0 100644 --- a/docs/source/examples_business_credit_card_fraud.rst +++ b/docs/source/examples_business_credit_card_fraud.rst @@ -31,7 +31,7 @@ This example uses the following version of VerticaPy: vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. @@ -85,7 +85,7 @@ Let's explore the data by displaying descriptive statistics of all the columns. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_creditcardfraud_describe.html -It'll be difficult to work on the principal components (V1 through V28) without knowing what they mean. The only features we can work on are ``Time`` and ``Amount``. +It'll be difficult to work on the principal components (``V1`` through ``V28``) without knowing what they mean. The only features we can work on are ``Time`` and ``Amount``. Let's convert the number of seconds elapsed to the correct date and time. We know that the records were ingested in September 2013, so we'll use that to create the new feature. @@ -269,7 +269,7 @@ Let's look at the correlation matrix and see which features influence our predic :suppress: :okwarning: - fig = creditcard.corr(width = 850, with_numbers = False) + fig = creditcard.corr(width = 850, height = 850, with_numbers = False) fig.write_html("SPHINX_DIRECTORY/figures/examples_creditcardfraud_corr_2.html") .. raw:: html @@ -316,7 +316,7 @@ Data Modeling Train/Test sets ++++++++++++++++ -Since we're dealing with time series data, we have to maintain time linearity. Our goal is to use the past to predict the future, so a k-fold cross-validation, for example, wouldn't make much sense here. +Since we're dealing with time series data, we have to maintain time linearity. Our goal is to use the past to predict the future, so a ``k-fold`` cross-validation, for example, wouldn't make much sense here. We will split the dataset into a train (day 1) and a test (day 2). @@ -328,7 +328,7 @@ We will split the dataset into a train (day 1) and a test (day 2). Supervision ++++++++++++ -Supervising would make this pretty easy since it would just be a binary classification problem. We can use different algorithms to optimize the prediction. Our dataset is unbalanced, so the AUC might be a good metric to evaluate the model. The PRC AUC would also be a relevant metric. +Supervising would make this pretty easy since it would just be a binary classification problem. We can use different algorithms to optimize the prediction. Our dataset is unbalanced, so the ``AUC`` might be a good metric to evaluate the model. The ``PRC`` ``AUC`` would also be a relevant metric. :py:mod:`~verticapy.machine_learning.vertica.linear_model.LogisticRegression` works well with monotonic relationships. Since we have a lot of independent features that correlate with the response, it should be a good first model to use. @@ -393,13 +393,13 @@ Unsupervised Learning There are many unsupervised learning techniques, but not all of them will be useful for detecting anomalies. Since there's no rigid mathematical definition for what an outlier is, finding anomalies becomes somewhat subjective. To solve this problem, we have to evaluate our constraints and needs. Do we need to find anomalies in real-time? Do we have a time constraint? -- **Real-time:** We don't have access to historical data, so we need an easy way to preprocess the data that is wholly independent from historical data, and the model must be simple to deploy at the source of the data stream. For example, we might use simple preprocessing techniques like normalization, standardization or One-Hot Encoding instead of more complex ones like windows, interpolation, or intersection. Isolation forests, k-means, robust :py:mod:`~verticapy.machine_learning.vertica.decomposition.PCA`, or global outlier detection using z-score would be ideal, whereas local outlier factor, DBSCAN, or other hard-to-deploy methods cannot be used. -- **Near Real-time:** We have access to historical data and our preprocessing method must be fast. The model has to be simple to score with. We can use any preprocessing technique as long as it is fast enough, which of course varies. Since this is still a real-time use case, we should still avoid any hard-to-deploy algorithms like DBSCAN or local outlier factor. +- **Real-time:** We don't have access to historical data, so we need an easy way to preprocess the data that is wholly independent from historical data, and the model must be simple to deploy at the source of the data stream. For example, we might use simple preprocessing techniques like normalization, standardization or One-Hot Encoding instead of more complex ones like windows, interpolation, or intersection. Isolation forests, :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans`, robust :py:mod:`~verticapy.machine_learning.vertica.decomposition.PCA`, or global outlier detection using z-score would be ideal, whereas local outlier factor, :py:mod:`~verticapy.machine_learning.vertica.cluster.DBSCAN`, or other hard-to-deploy methods cannot be used. +- **Near Real-time:** We have access to historical data and our preprocessing method must be fast. The model has to be simple to score with. We can use any preprocessing technique as long as it is fast enough, which of course varies. Since this is still a real-time use case, we should still avoid any hard-to-deploy algorithms like :py:mod:`~verticapy.machine_learning.vertica.cluster.DBSCAN` or local outlier factor. - **No time constraint:** We can use any techniques we want. -Due to the complexity of the computations, anomalies are difficult to detect in the context of "Big Data." We have three efficient methods for that case: +Due to the complexity of the computations, anomalies are difficult to detect in the context of "Big Data". We have three efficient methods for that case: -- **Machine Learning:** We need to use easily-deployable algorithms to perform real-time fraud detection. Isolation forests and ``k-means`` can be easily deployed and they work well for detecting anomalies. +- **Machine Learning:** We need to use easily-deployable algorithms to perform real-time fraud detection. Isolation forests and :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` can be easily deployed and they work well for detecting anomalies. - **Rules & Thresholds:** The z-score can be an efficient solution for detecting global outliers. - **Decomposition:** Robust :py:mod:`~verticapy.machine_learning.vertica.decomposition.PCA` is another technique for detecting outliers. @@ -455,7 +455,7 @@ For the rest of this example, we'll investigate labels and how they can help us k-means Clustering +++++++++++++++++++ -We begin by examining ``k-means`` clustering, which partitions the data into k clusters. +We begin by examining :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` clustering, which partitions the data into k clusters. We can use an elbow curve to find a suitable number of clusters. We can then add more clusters then the amount suggested by the :py:func:`~verticapy.machine_learning.model_selection.elbow` curve to create clusters mainly composed of anomalies. Clusters with relatively fewer elements can then be investigated by an expert to label the anomalies. @@ -535,7 +535,7 @@ Let's direct our attention to the smallest clusters. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_creditcardfraud_groupby_ml.html -Notice that clusters with fewer elemenets tend to contain much more fraudulent events than the others. This methodology makes ``k-means`` a good algorithm for catching collective outliers. Combining ``k-means`` with other techniques like z-score, we can find most of the outliers of the distribution. +Notice that clusters with fewer elemenets tend to contain much more fraudulent events than the others. This methodology makes :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` a good algorithm for catching collective outliers. Combining :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` with other techniques like ``Z-score``, we can find most of the outliers of the distribution. Outliers of the distribution +++++++++++++++++++++++++++++ @@ -603,7 +603,7 @@ Let's use the ``Z-score`` to detect global outliers of the distribution. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_creditcardfraud_ml_outliers_plot_3.html -We can see that we can caught more than 71% of the fraudulent activity in less than 1% of the dataset. +We can see that we can caught more than ``71%`` of the fraudulent activity in less than ``1%`` of the dataset. Neighbors ++++++++++ @@ -637,7 +637,7 @@ Other algorithms could be used to solve the problem with more precision if we co .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_creditcardfraud_ml_lof_plot_1.html -We can catch outliers with a neighbors score. Again, the main problem with these sorts of algorithms is that what they have in precision, they lack in speed, which makes them unsuitable for scoring new data. This is why it's important to focus on scalable techniques like ``k-means``. +We can catch outliers with a neighbors score. Again, the main problem with these sorts of algorithms is that what they have in precision, they lack in speed, which makes them unsuitable for scoring new data. This is why it's important to focus on scalable techniques like :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans`. Other Techniques +++++++++++++++++ diff --git a/docs/source/examples_business_football.rst b/docs/source/examples_business_football.rst index be947fe32..09ab06e88 100644 --- a/docs/source/examples_business_football.rst +++ b/docs/source/examples_business_football.rst @@ -28,7 +28,7 @@ This example uses the following version of VerticaPy: vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. @@ -562,7 +562,7 @@ Let's export the result to our Vertica database. Team KPIs ++++++++++ -We use just two variables to track teams: away_team and home_team. This makes it a bit difficult to compute new features. We need to duplicate the dataset and intervert the two teams. This way, we can compute KPIs using a partition by the first team to avoid double-counting any games. +We use just two variables to track teams: ``away_team`` and ``home_team``. This makes it a bit difficult to compute new features. We need to duplicate the dataset and intervert the two teams. This way, we can compute KPIs using a partition by the first team to avoid double-counting any games. .. code-block:: python @@ -907,7 +907,7 @@ Let's export the result to our Vertica database. Team Rankings with k-means --------------------------- -To compute a ``k-means`` model, we need to find a value for ``k``. Let's draw an :py:func:`~verticapy.machine_learning.model_selection.elbow` curve to find a suitable number of clusters. +To compute a :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` model, we need to find a value for ``k``. Let's draw an :py:func:`~verticapy.machine_learning.model_selection.elbow` curve to find a suitable number of clusters. .. code-block:: python @@ -1962,7 +1962,7 @@ It's time to make predictions about the outcomes of games. We have a lot of vari .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_football_clean_kpi_ml_1.html -Our model is excellent! 57% of accuracy on 3 categories - it's almost twice as good as a random model. +Our model is excellent! ``57%`` of accuracy on 3 categories - it's almost twice as good as a random model. .. ipython:: python diff --git a/docs/source/examples_business_movies.rst b/docs/source/examples_business_movies.rst index 6444a2f73..f14fca4a2 100644 --- a/docs/source/examples_business_movies.rst +++ b/docs/source/examples_business_movies.rst @@ -35,7 +35,7 @@ This example uses the following version of VerticaPy: vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. @@ -90,7 +90,7 @@ First, let's explore the dataset. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_movies_describe_cat.html -We can drop the 'description' and 'notes' columns since these fields are empty for most of our dataset. +We can drop the ``description`` and ``notes`` columns since these fields are empty for most of our dataset. .. code-block:: python @@ -855,7 +855,7 @@ Great, our results are more consistent. Psycho, Pulp Fiction, and The Godfather Machine Learning : Creating Movie Clusters ------------------------------------------- -Since ``k-means`` clustering is sensitive to unnormalized data, let's normalize our new predictors. +Since :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` clustering is sensitive to unnormalized data, let's normalize our new predictors. .. code-block:: python @@ -917,7 +917,7 @@ Let's compute the :py:func:`~verticapy.machine_learning.model_selection.elbow` c .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_movies_filmtv_elbow_plot.html -By looking at the elbow curve, we can choose 15 clusters. Let's create a ``k-means`` model. +By looking at the elbow curve, we can choose 15 clusters. Let's create a :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` model. .. ipython:: python diff --git a/docs/source/examples_business_smart_meters.rst b/docs/source/examples_business_smart_meters.rst index 0145cafed..996194148 100644 --- a/docs/source/examples_business_smart_meters.rst +++ b/docs/source/examples_business_smart_meters.rst @@ -36,7 +36,7 @@ This example uses the following version of VerticaPy: vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. @@ -135,7 +135,7 @@ Data Exploration and Preparation Predicting energy consumption in households is very important. Surges in electricity use could cause serious power outages. In our case, we'll be using data on general household energy consumption in Ireland to predict consumption at various times. -In order to join the different data sources, we need to assume that the weather will be approximately the same across the entirety of Ireland. We'll use the date and time as the key to join 'sm_weather' and 'sm_consumption'. +In order to join the different data sources, we need to assume that the weather will be approximately the same across the entirety of Ireland. We'll use the date and time as the key to join ``sm_weather`` and ``sm_consumption``. Joining different datasets with interpolation ++++++++++++++++++++++++++++++++++++++++++++++ @@ -174,7 +174,7 @@ In VerticaPy, you can interpolate joins; Vertica will find the closest timestamp Segmenting Latitude & Longitude using Clustering +++++++++++++++++++++++++++++++++++++++++++++++++ -The dataset 'sm_meters' is pretty important. In particular, the type of residence is probably a good predictor for electricity usage. We can create clusters of the different regions with k-means clustering based on longitude and latitude. Let's find the most suitable ``k`` using an elbow curve and scatter plot. +The dataset ``sm_meters`` is pretty important. In particular, the type of residence is probably a good predictor for electricity usage. We can create clusters of the different regions with :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` clustering based on longitude and latitude. Let's find the most suitable ``k`` using an elbow curve and scatter plot. .. code-block:: python @@ -239,7 +239,7 @@ Based on the scatter plot, five seems like the optimal number of clusters. Let's .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_sm_meters_elbow_1.html -The elbow curve seems to confirm that five is the optimal number of clusters, so let's create a ``k-means`` model with that in mind. +The elbow curve seems to confirm that five is the optimal number of clusters, so let's create a :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` model with that in mind. .. ipython:: python @@ -353,7 +353,7 @@ Let's take care of our missing values. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_sm_consumption_weather_region_count_percent_table.html -The variable 'value' has a few missing values that we can drop. +The variable ``value`` has a few missing values that we can drop. .. code-block:: python @@ -375,7 +375,7 @@ The variable 'value' has a few missing values that we can drop. Interpolation & Aggregations +++++++++++++++++++++++++++++ -Since power outages seem relatively common in each area, and the ``value`` represents the electricity consumed during 30 minute intervals (in kWh), it'd be a good idea to interpolate and aggregate the data to get a monthly average in electricity consumption per region. +Since power outages seem relatively common in each area, and the ``value`` represents the electricity consumed during 30 minute intervals (in ``kWh``), it'd be a good idea to interpolate and aggregate the data to get a monthly average in electricity consumption per region. Let's save our new dataset in the Vertica database. @@ -800,7 +800,7 @@ Let's create our model. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_sm_consumption_model_report_9.html -The model seems to be good with an adjusted R2 of 77.5%, and the F-Statistic indicates that at least one of the two predictors is useful. Let's look at the residual plot. +The model seems to be good with an adjusted ``R2`` of ``77.5%``, and the F-Statistic indicates that at least one of the two predictors is useful. Let's look at the residual plot. .. code-block:: python @@ -833,7 +833,7 @@ Looking at the residual plot, we can see that the error variance varies by quite het_breuschpagan(sm_consumption_final, "residual", predictors) -The ``p-value`` is 4.81% and sits around the 5% threshold, so we can't really draw any conclusions. +The ``p-value`` is ``4.81%`` and sits around the ``5%`` threshold, so we can't really draw any conclusions. Let's look at the entire regression report. @@ -854,7 +854,7 @@ Let's look at the entire regression report. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_sm_consumption_model_report_10.html -Our model is very good; its median absolute error is around 13kWh. +Our model is very good; its median absolute error is around ``13kWh``. With this model, we can make predictions about the energy consumption of households per region. If the usage exceeds what the model predicts, we can raise an alert and respond, for example, by regulating the electricity distributed to the region. Conclusion diff --git a/docs/source/examples_business_spotify.rst b/docs/source/examples_business_spotify.rst index 4277981cf..63c468cd0 100644 --- a/docs/source/examples_business_spotify.rst +++ b/docs/source/examples_business_spotify.rst @@ -41,7 +41,7 @@ Categorical: - **release_date**: date of release (yyyy-mm-dd). - **name**: track name. -The "artists" dataset (artists.csv) has the following features: +The ``artists`` dataset (``artists.csv``) has the following features: - **id**: ID of the artist. - **name**: artist name. @@ -69,7 +69,7 @@ Start by importing VerticaPy and loading the SQL extension, which allows you to %load_ext verticapy.sql -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. @@ -155,7 +155,7 @@ Since we are only focusing on Polish artists in this subset of data, let us save Data Exploration ----------------- -We can visualize the top 60 most-followed Polish artists with a bar chart. +We can visualize the top ``60`` most-followed Polish artists with a bar chart. .. code-block:: python @@ -381,7 +381,7 @@ To expand our analysis, let's take into account some descriptive features. Since Additionally, we manipulate our data a bit to make things easier later on: -- converting the duration unit from 'ms' to 'minute' +- converting the duration unit from ``ms`` to ``minute`` - extracting the year from the date. .. code-block:: python @@ -837,7 +837,7 @@ Grouping means clustering, so we use an :py:func:`~verticapy.machine_learning.mo .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_spotify_lr_elbow.html -Let's define and use the Vertica ``k-means`` algorithm to create a model that can group artists together. +Let's define and use the Vertica :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` algorithm to create a model that can group artists together. .. ipython:: python :okwarning: @@ -850,7 +850,7 @@ Let's define and use the Vertica ``k-means`` algorithm to create a model that ca n_cluster = 7, ) -We can train our new model on the "artists_features" relation we saved earlier. +We can train our new model on the ``artists_features`` relation we saved earlier. .. ipython:: python @@ -915,4 +915,4 @@ Let's see how our model groups these artists together: Conclusion ----------- -We were able to predict the popularity Polish songs with a :py:mod:`~verticapy.machine_learning.vertica.ensemble.RandomForestRegressor` model suggested by :py:mod:`~verticapy.machine_learning.vertica.automl.AutoML`. We then created a ``k-means`` model to group artists into "genres" (clusters) based on the feature-commonalities in their tracks. \ No newline at end of file +We were able to predict the popularity Polish songs with a :py:mod:`~verticapy.machine_learning.vertica.ensemble.RandomForestRegressor` model suggested by :py:mod:`~verticapy.machine_learning.vertica.automl.AutoML`. We then created a :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` model to group artists into ``genres`` (clusters) based on the feature-commonalities in their tracks. \ No newline at end of file diff --git a/docs/source/examples_learn_commodities.rst b/docs/source/examples_learn_commodities.rst index daacbc46c..e60600214 100644 --- a/docs/source/examples_learn_commodities.rst +++ b/docs/source/examples_learn_commodities.rst @@ -26,7 +26,7 @@ This example uses the following version of VerticaPy: vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. diff --git a/docs/source/examples_learn_iris.rst b/docs/source/examples_learn_iris.rst index 6aa73fb84..43e152904 100644 --- a/docs/source/examples_learn_iris.rst +++ b/docs/source/examples_learn_iris.rst @@ -321,7 +321,7 @@ Let's evaluate our final model (the combination of two :py:mod:`~verticapy.machi iris["score"].avg() -We have a great model with an accuracy of 96% on an entirely balanced dataset. +We have a great model with an accuracy of ``96%`` on an entirely balanced dataset. Conclusion ----------- diff --git a/docs/source/examples_learn_pokemon.rst b/docs/source/examples_learn_pokemon.rst index 609d1581c..ac7b2ffbc 100644 --- a/docs/source/examples_learn_pokemon.rst +++ b/docs/source/examples_learn_pokemon.rst @@ -280,7 +280,7 @@ Many variables are correlated to the response column. We have enough information Machine Learning ----------------- -Some really important features are categorical. Random forest can handle them. Besides, we need trees deep enough to compare all the different types. +Some really important features are categorical. ``Random Forest`` can handle them. Besides, we need trees deep enough to compare all the different types. .. code-block:: python @@ -318,7 +318,7 @@ Some really important features are categorical. Random forest can handle them. B .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_pokemon_cv.html -We have an excellent model with an average AUC of more than 99%. Let's create a model with the entire dataset and look at the importance of each feature. +We have an excellent model with an average ``AUC`` of more than ``99%``. Let's create a model with the entire dataset and look at the importance of each feature. .. code-block:: python diff --git a/docs/source/examples_learn_titanic.rst b/docs/source/examples_learn_titanic.rst index 090aea846..06c37ce9e 100644 --- a/docs/source/examples_learn_titanic.rst +++ b/docs/source/examples_learn_titanic.rst @@ -217,7 +217,7 @@ The "sibsp" column represents the number of siblings for each passenger, while t titanic["family_size"] = titanic["parch"] + titanic["sibsp"] + 1 -Let's move on to outliers. We have several tools for locating outliers (:py:mod:`~verticapy.machine_learning.vertica.LocalOutlierFactor`, :py:mod:`~verticapy.machine_learning.vertica.DBSCAN`, ``k-means``...), but we'll just use winsorization in this example. Again, "fare" has many outliers, so we'll start there. +Let's move on to outliers. We have several tools for locating outliers (:py:mod:`~verticapy.machine_learning.vertica.LocalOutlierFactor`, :py:mod:`~verticapy.machine_learning.vertica.DBSCAN`, :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans`...), but we'll just use winsorization in this example. Again, "fare" has many outliers, so we'll start there. .. code-block:: python @@ -458,7 +458,7 @@ One of our predictors is categorical: the passenger title. Some of these predict .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_titanic_table_ml_cv.html -This dataset is pretty unbalanced so we'll use an ``AUC`` to evaluate it. Looking at our table, our model has an average AUC of more than 0.9, so our model is quite good. +This dataset is pretty unbalanced so we'll use an ``AUC`` to evaluate it. Looking at our table, our model has an average ``AUC`` of more than 0.9, so our model is quite good. We can now build a model with the entire dataset. diff --git a/docs/source/examples_learn_winequality.rst b/docs/source/examples_learn_winequality.rst index cf1864cda..ad2186126 100644 --- a/docs/source/examples_learn_winequality.rst +++ b/docs/source/examples_learn_winequality.rst @@ -31,7 +31,7 @@ This example uses the following version of VerticaPy: vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. diff --git a/docs/source/examples_understand.rst b/docs/source/examples_understand.rst index 50aca7388..a66640064 100644 --- a/docs/source/examples_understand.rst +++ b/docs/source/examples_understand.rst @@ -24,8 +24,6 @@ Understand environment. Engage with the data-driven exploration of educational dynamics. -.. grid:: 1 1 2 2 - .. grid-item:: .. card:: Amazon @@ -45,8 +43,6 @@ Understand each region. Witness the power of data-driven foresight as we navigate the dynamic landscape of the Amazon. -.. grid:: 1 1 2 2 - .. grid-item:: .. card:: Covid 19 @@ -66,10 +62,9 @@ Understand Experience the precision of our predictive analytics as we navigate through the complexities of COVID-19 trends. - .. toctree:: :maxdepth: 1 examples_understand_amazon examples_understand_covid19 - examples_understand_africa_education + examples_understand_africa_education \ No newline at end of file diff --git a/docs/source/examples_understand_africa_education.rst b/docs/source/examples_understand_africa_education.rst index a40289350..3fd5ac61a 100644 --- a/docs/source/examples_understand_africa_education.rst +++ b/docs/source/examples_understand_africa_education.rst @@ -84,7 +84,7 @@ This example uses the following version of VerticaPy: vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. @@ -244,7 +244,7 @@ These clusters can be used as inputs by our model. .. raw:: html :file: SPHINX_DIRECTORY/figures/examples_africa_elbow.html -Eight seems to be a suitable number of clusters. Let's compute a ``k-means`` model. +Eight seems to be a suitable number of clusters. Let's compute a :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` model. .. code-block:: python diff --git a/docs/source/examples_understand_amazon.rst b/docs/source/examples_understand_amazon.rst index fcc29cdb0..518255db6 100644 --- a/docs/source/examples_understand_amazon.rst +++ b/docs/source/examples_understand_amazon.rst @@ -22,7 +22,7 @@ This example uses the following version of VerticaPy: vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. diff --git a/docs/source/examples_understand_covid19.rst b/docs/source/examples_understand_covid19.rst index a039280ac..0432b429e 100644 --- a/docs/source/examples_understand_covid19.rst +++ b/docs/source/examples_understand_covid19.rst @@ -25,7 +25,7 @@ This example uses the following version of VerticaPy: vp.__version__ -Connect to Vertica. This example uses an existing connection called "VerticaDSN." +Connect to Vertica. This example uses an existing connection called ``VerticaDSN``. For details on how to create a connection, see the :ref:`connection` tutorial. You can skip the below cell if you already have an established connection. diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst index a841f7aff..839b9459b 100644 --- a/docs/source/getting_started.rst +++ b/docs/source/getting_started.rst @@ -54,7 +54,7 @@ arming you and your customers with predictive business insights faster than any Vertica provides a unified analytics platform across major public clouds and on-premises data centers and integrates data in cloud object storage and HDFS without forcing you to move any of your data. -To learn more about the Vertica database, check out the `Vertica Official Website `_. +To learn more about the Vertica database, check out the `Vertica Official Website `_. If you already have Vertica installed, you can skip this step. Otherwise, you have some options for trying out Vertica for free. diff --git a/docs/source/performance_vertica.rst b/docs/source/performance_vertica.rst index 6ad5d0987..dfd5bb808 100644 --- a/docs/source/performance_vertica.rst +++ b/docs/source/performance_vertica.rst @@ -4,9 +4,8 @@ Vertica ========= - Query Profiler -^^^^^^^^^^^^^^^^^^ +^^^^^^^^^^^^^^^ .. currentmodule:: verticapy.performance.vertica.qprof @@ -46,7 +45,6 @@ Query Profiler QueryProfiler.step QueryProfiler.to_html - Query Profiler Interface ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -87,10 +85,8 @@ Query Profiler Interface QueryProfilerInterface.step QueryProfilerInterface.to_html - - Query Profiler Comparison -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +^^^^^^^^^^^^^^^^^^^^^^^^^^ .. currentmodule:: verticapy.performance.vertica.qprof_interface @@ -106,6 +102,4 @@ Query Profiler Comparison .. autosummary:: :toctree: api/ - QueryProfilerComparison.display - QueryProfilerComparison.get_qplan_tree - + QueryProfilerComparison.get_qplan_tree \ No newline at end of file diff --git a/docs/source/user_guide_data_exploration_charts.rst b/docs/source/user_guide_data_exploration_charts.rst index f13e485d6..ecd9427e0 100644 --- a/docs/source/user_guide_data_exploration_charts.rst +++ b/docs/source/user_guide_data_exploration_charts.rst @@ -321,7 +321,7 @@ Similarly, we can plot a bubble plot: .. raw:: html :file: SPHINX_DIRECTORY/figures/user_guides_data_exploration_iris_scatter_bubble.html -For more information on scatter look at :py:func:`verticapy.vDataFrame.scatter` +For more information on scatter look at :py:func:`~verticapy.vDataFrame.scatter` Hexbin plots can be useful for generating heatmaps. These summarize data in a similar way to scatter plots, but compute aggregations to get the final results. diff --git a/docs/source/user_guide_data_exploration_descriptive_statistics.rst b/docs/source/user_guide_data_exploration_descriptive_statistics.rst index c81fbc11f..c042c116f 100644 --- a/docs/source/user_guide_data_exploration_descriptive_statistics.rst +++ b/docs/source/user_guide_data_exploration_descriptive_statistics.rst @@ -8,7 +8,20 @@ The easiest way to understand data is to aggregate it. An aggregation is a number or a category which summarizes the data. VerticaPy lets you compute all well-known aggregation in a single line. -The :py:func:`~verticapy.vDataFrame.agg` method is the best way to compute multiple aggregations on multiple columns at the same time. +The :py:func:`~verticapy.vDataFrame.aggregate` method is the best way to compute multiple aggregations on multiple columns at the same time. + +.. ipython:: python + :suppress: + + import inspect + import re + + def help(obj): + signature = f"Help on function {obj.__name__} in module {obj.__module__}:\n\n{obj.__name__}{inspect.signature(obj)}" + doc = inspect.getdoc(obj) + if doc: + short_doc = re.split(r"\n\s*Examples\s*[-=]*\s*\n", doc)[0] + print(f"{signature}\n\n{short_doc}") .. ipython:: python @@ -36,7 +49,7 @@ Let's use the `churn dataset `_. In order to use this new functionality, we first need to install the ``ODBC`` driver and manager, as well as configure ``DBLINK`` on all nodes of the cluster. Configuration entails three files: -- dblink.cids -- odbc.ini -- odbcinst.ini - +- ``dblink.cids`` +- ``odbc.ini`` +- ``odbcinst.ini`` These files provide the host server address, username, and password, as well as the database name that we want to access. In future versions, we are planning to simplify this process and automate the creation of these files. @@ -29,8 +28,6 @@ Connecting to an External Database # Importing VerticaPy import verticapy as vp - - We first need to provide the connection information that we have set up in the Connection Identifier Database file (``dblink.cids``). We can select a special character symbol to identify this connection. Let's try to set up a connection with an external ``PostgreSQL`` database, which we name ``pgdb``. The connection details for ``pgdb``, including server name, user name etc., are in the configuration files mentioned in the introduction section. @@ -44,13 +41,13 @@ Let's try to set up a connection with an external ``PostgreSQL`` database, which symbol = "&", ) -Creating a vDataFrame ---------------------- +Creating a :py:mod:`~verticapy.vDataFrame` +------------------------------------------- + +We can create a :py:mod:`~verticapy.vDataFrame` from a table stored in an external +database by setting the ``external`` parameter to ``True``. -We can create a vDataFrame from a table stored in an external -database by setting the ``external`` parameter to True. -SQL can be used to fetch required data, and we can provide -an identifying symbol that can be used for fetching perform queries with SQL. +SQL can be used to fetch required data, and we can provide an identifying symbol that can be used for fetching perform queries with SQL. .. code-block:: python @@ -79,8 +76,8 @@ an identifying symbol that can be used for fetching perform queries with SQL. .. raw:: html :file: SPHINX_DIRECTORY/figures/ug_fs_dblink_airports_table.html +All :py:mod:`~verticapy.vDataFrame` methods are available for this imported table. -All vDataFrame functions are available for this imported table. For example, we can get all the column names: .. ipython:: python @@ -90,13 +87,11 @@ For example, we can get all the column names: Or the column data types: - .. ipython:: python # Get data types of all columns inside the dataset tab_data.dtypes() - Or the count of the datapoints: .. code-block:: python @@ -104,26 +99,21 @@ Or the count of the datapoints: # Counting all elements inside each column tab_data.count() - .. note:: - Every time we perform these calculations or call the vDataFrame, it runs the SQL - query to fetch all the data from the external database. After retrieving the - entire table, the operations are computed by Vertica. In order to push the - queries to a remote database, we can use the option ``sql_push_ext``. When we - create a vDataFrame with this option activated, all the aggregations are done - on the external database using SQL. + Every time we perform these calculations or call the :py:mod:`~verticapy.vDataFrame`, it runs the SQL query to fetch all the data from the external database. After retrieving the entire table, the operations are computed by Vertica. In order to push the queries to a remote database, we can use the option ``sql_push_ext``. When we create a :py:mod:`~verticapy.vDataFrame` with this option activated, all the aggregations are done on the external database using SQL. .. code-block:: python # Creating a vDataFrame and setting sql_push_ext to True, which tries # to push SQL queries to external database (where possible). - Ext_Table=vp.vDataFrame(input_relation = "airports", - external = True, - symbol = "&", - sql_push_ext = True) - Ext_Table - + Ext_Table = vp.vDataFrame( + input_relation = "airports", + external = True, + symbol = "&", + sql_push_ext = True, + ) + Ext_Table.head(100) If we look at the SQL generated in background, we can see that it pushes the aggregation query to the database. @@ -158,7 +148,7 @@ Let's look at the count query again, and see how VerticaPy is pushing it to the rowset=500) OVER () -Let's also look at the "min" method: +Let's also look at the :py:func:`~verticapy.vDataFrame.min` method: .. code-block:: python @@ -182,11 +172,9 @@ Let's also look at the "min" method: For the above examples, the queries were pushed to the external database. -If the function is unique to Vertica, it automatically fetches -the data from the external database to compute on the Vertica server. +If the function is unique to Vertica, it automatically fetches the data from the external database to compute on the Vertica server. -Let's try an example with the :py:func:`~verticapy.vDataFrame.describe` function, which is a -unique Vertica function. +Let's try an example with the :py:func:`~verticapy.vDataFrame.describe` function, which is a unique Vertica function. .. code-block:: python @@ -249,13 +237,11 @@ Using SQL Magic Cells %load_ext verticapy.sql We can use magic cells to call external tables using special characters -like ``$$$`` and ``%%%``. If we have multiple external databases, -we can specify special characters for each. +like ``$$$`` and ``%%%``. If we have multiple external databases, we can specify special characters for each. This makes writing queries a lot more convenient and visually appealing! -Now we will try to get fetch data from our external database ``pgdb``, -whose special character is ``&``. +Now we will try to get fetch data from our external database ``pgdb``, whose special character is ``&``. .. code-block:: python @@ -263,12 +249,10 @@ whose special character is ``&``. /* Getting all data from airports table which is placed in the PostgreSQL database represented by "&". */ SELECT * FROM &&& airports &&&; - .. raw:: html :file: SPHINX_DIRECTORY/figures/ug_fs_dblink_airports_table.html -To perform all regular queries, all we need to do is -call the table with its name inside three special characters. +To perform all regular queries, all we need to do is call the table with its name inside three special characters. We'll now try out some queries: @@ -294,8 +278,7 @@ Count the elements inside the table: .. raw:: html :file: SPHINX_DIRECTORY/figures/ug_fs_dblink_airports_count.html - -Find the IATA_CODE where CITY is "Allentown": +Find the ``IATA_CODE`` where ``CITY`` is ``Allentown``: .. code-block:: python @@ -324,10 +307,9 @@ Find the IATA_CODE where CITY is "Allentown": .. note:: Any query that we write inside the ``&&&`` signs is also sent to the external database to be run. -So, instead of just calling the whole table, we can query it using -the same special character padding. +So, instead of just calling the whole table, we can query it using the same special character padding. -For example, let's select all elements inside the airports table: +For example, let's select all elements inside the ``airports`` table: .. code-block:: python @@ -381,13 +363,12 @@ You can connect and use multiple datasets from different databases. In this example we will get: -- Airline data from PostgreSQL -- Airport data from MySQL +- Airline data from ``PostgreSQL`` +- Airport data from ``MySQL`` - Flights data from Vertica The datasets can be found `here `_. - Airline Data in PostgreSQL +++++++++++++++++++++++++++ @@ -399,10 +380,10 @@ We can set up a new connection in just one line by referencing the alias inside vp.set_external_connection( cid="pgdb", rowset=500, - symbol="$" + symbol="$", ) -Let's look at the airline table that we have in our postgreSQL database. +Let's look at the airline table that we have in our ``postgreSQL`` database. .. code-block:: python @@ -430,9 +411,7 @@ Let's look at the airline table that we have in our postgreSQL database. Airports Data in MySQL ++++++++++++++++++++++ -We can create another new connection by providing the cid reference -for our MySQL database. We'll also provide a unique special character, -which is not used for any other connection. +We can create another new connection by providing the ``cid`` reference for our ``MySQL`` database. We'll also provide a unique special character, which is not used for any other connection. .. code-block:: python @@ -440,10 +419,10 @@ which is not used for any other connection. vp.set_external_connection( cid="mysql", rowset=500, - symbol="&" + symbol="&", ) -Let's take a look at the airports table that we have in our MySQL database. +Let's take a look at the airports table that we have in our ``MySQL`` database. .. code-block:: python @@ -451,8 +430,6 @@ Let's take a look at the airports table that we have in our MySQL database. /* Fetch all the data from the table airports in "mysql" database */ SELECT * FROM &&& airports &&&; - - .. raw:: html :file: SPHINX_DIRECTORY/figures/ug_fs_dblink_airports_table.html @@ -497,12 +474,10 @@ Joins and Queries Across Multiple Databases Now we can run queries that execute through multiple sources. -Let's try to find the ``TAIL_NUMBER`` and ``Departing City`` for -all the flights by joining the two tables: - -- flight_vertica (stored in Vertica) -- airports (stored in MySQL) +Let's try to find the ``TAIL_NUMBER`` and ``Departing City`` for all the flights by joining the two tables: +- ``flight_vertica`` (stored in Vertica) +- ``airports`` (stored in ``MySQL``) .. code-block:: python @@ -530,11 +505,10 @@ all the flights by joining the two tables: .. raw:: html :file: SPHINX_DIRECTORY/figures/ug_fs_dblink_multi_join.html -Let's try another query to find the ``TAIL_NUMBER`` and ``AIRLINE`` -of all the flights by joining the two tables: +Let's try another query to find the ``TAIL_NUMBER`` and ``AIRLINE`` of all the flights by joining the two tables: -- flight_vertica (stored in Vertica) -- airline (stored in PostgreSQL) +- ``flight_vertica`` (stored in Vertica) +- ``airline`` (stored in ``PostgreSQL``) .. code-block:: python @@ -567,9 +541,9 @@ We can even try queries that require multiple joins. In the following example, we try to get the ``TAIL_NUMBER``, ``AIRLINE``, and ``CITY`` details for all the flights by joining: -- flight_local table (stored in Vertica) -- airline table (stored in PostgreSQL) -- airports table (stored in MySQL) +- ``flight_local`` table (stored in Vertica) +- ``airline`` table (stored in ``PostgreSQL``) +- ``airports`` table (stored in ``MySQL``) .. code-block:: python @@ -604,7 +578,7 @@ In the following example, we try to get the ``TAIL_NUMBER``, Pandas.DataFrame ----------------- -The joins also work with pandas.Dataframe. We can perform the same query that required multiple joins, but now with a local Pandas dataframe. +The joins also work with ``pandas.Dataframe``. We can perform the same query that required multiple joins, but now with a local Pandas dataframe. We can read a local passengers CSV file using :py:func:`~verticapy.read_csv` or we could create an artificial dataset as well. @@ -652,11 +626,10 @@ We can read a local passengers CSV file using :py:func:`~verticapy.read_csv` or We can now perform the same query involving the three tables: -- flight_vertica table (stored in Vertica) -- passengers_pandas table (pandas.DataFrame stored in-memory) -- airline table (stored in PostgreSQL) -- airports table (stored in MySQL) - +- ``flight_vertica`` table (stored in Vertica) +- ``passengers_pandas`` table (``pandas.DataFrame`` stored in-memory) +- ``airline`` table (stored in ``PostgreSQL``) +- ``airports`` table (stored in ``MySQL``) .. code-block:: python @@ -701,9 +674,9 @@ We can now perform the same query involving the three tables: :file: SPHINX_DIRECTORY/figures/ug_fs_dblink_multi_mega_join.html Conclusion ------------- +----------- -With the combination of Verticapy and DBLINK, we can now work with multiple datasets stored in different databases. We can work simultaneously with external tables, Vertica tables, and Pandas DataFrame in a **single query**! There is no need to materialize the table before use because it's all taken care of in the background. +With the combination of Verticapy and ``DBLINK``, we can now work with multiple datasets stored in different databases. We can work simultaneously with external tables, Vertica tables, and Pandas DataFrame in a **single query**! There is no need to materialize the table before use because it's all taken care of in the background. The cherry on the cake is the ease-of-use that is enabled by VerticaPy and its Python-like syntax. diff --git a/docs/source/user_guide_full_stack_linear_regression.rst b/docs/source/user_guide_full_stack_linear_regression.rst index a10a04f8e..82a9ae04d 100644 --- a/docs/source/user_guide_full_stack_linear_regression.rst +++ b/docs/source/user_guide_full_stack_linear_regression.rst @@ -564,14 +564,26 @@ We can verify the Gauss-Markov assumptions with our :py:mod:`~verticapy.machine_ .. code-block:: python - africa_pca.corr() + africa_pca.corr( + columns = + [ + "student_score", + "col1", + "col2", + "col3", + "col4", + "col5", + "col6", + "col7", + ], + ) .. ipython:: python :suppress: :okwarning: vp.set_option("plotting_lib","plotly") - fig = africa_pca.corr(with_numbers = False) + fig = africa_pca.corr(columns = ["student_score", "col1", "col2", "col3", "col4", "col5", "col6", "col7"], with_numbers = False) fig.write_html("SPHINX_DIRECTORY/figures/ug_fs_plot_lr_18.html") .. raw:: html diff --git a/docs/source/user_guide_full_stack_udf.rst b/docs/source/user_guide_full_stack_udf.rst index 94d51fc0c..ed86922f4 100644 --- a/docs/source/user_guide_full_stack_udf.rst +++ b/docs/source/user_guide_full_stack_udf.rst @@ -4,7 +4,7 @@ User-Defined Functions ======================= -Vertica is an analytical database with many advanced functions. While Vertica obviously doesn't have every function, it comes pretty close by implementing a Python SDK that allows users to create and import their own "lambda" functions into Vertica. +Vertica is an analytical database with many advanced functions. While Vertica obviously doesn't have every function, it comes pretty close by implementing a Python SDK that allows users to create and import their own ``lambda`` functions into Vertica. While the SDK is somewhat difficult to use, VerticaPy makes it easy by generating UDFs from python code and importing them into Vertica for you. diff --git a/docs/source/user_guide_full_stack_vdataframe_magic.rst b/docs/source/user_guide_full_stack_vdataframe_magic.rst index bc6b0a3e9..b5dbb1c1d 100644 --- a/docs/source/user_guide_full_stack_vdataframe_magic.rst +++ b/docs/source/user_guide_full_stack_vdataframe_magic.rst @@ -4,7 +4,7 @@ The ``Magic`` Methods of the vDataFrame ====================================== -VerticaPy 0.3.2 introduces the ``Magic`` methods, which offer some additional flexilibility for mathematical operations in the :py:mod:`~verticapy.vDataFrame`. These methods let you handle many operations in a ``pandas-like`` or Pythonic style. +VerticaPy 0.3.2 introduces the ``Magic`` methods, which offer some additional flexibility for mathematical operations in the :py:mod:`~verticapy.vDataFrame`. These methods let you handle many operations in a ``pandas-like`` or Pythonic style. .. code-block:: ipython @@ -94,7 +94,7 @@ You can also create features from various mathematical functions. Conditional Operators ---------------------- -You can now filter your data with conditional operators like and (``&``), or (``|``), equals (``==``), not equals (!=), and more! +You can now filter your data with conditional operators like and (``&``), or (``|``), equals (``==``), not equals (``!=``), and more! Equal Operator (==) ++++++++++++++++++++ diff --git a/docs/source/user_guide_introduction_best_practices.rst b/docs/source/user_guide_introduction_best_practices.rst index 1a752c33f..b7f80abb4 100644 --- a/docs/source/user_guide_introduction_best_practices.rst +++ b/docs/source/user_guide_introduction_best_practices.rst @@ -32,7 +32,7 @@ In the following examples, we'll demonstrate how to create a :py:mod:`~verticapy .. raw:: html :file: SPHINX_DIRECTORY/figures/user_guide_introduction_best_practices_laod_titanic.html -Supposing we are only interested in the ``survived``, ``pclass``, ``age``, ``parch``, and ``sibsp`` columns, we can create a vDataFrame with just those columns by specifying them in the usecols parameter: +Supposing we are only interested in the ``survived``, ``pclass``, ``age``, ``parch``, and ``sibsp`` columns, we can create a :py:mod:`~verticapy.vDataFrame` with just those columns by specifying them in the usecols parameter: .. code-block:: python @@ -60,9 +60,9 @@ Supposing we are only interested in the ``survived``, ``pclass``, ``age``, ``par .. raw:: html :file: SPHINX_DIRECTORY/figures/user_guide_introduction_best_practices_laod_titanic_selective.html -If we run the :py:func:`~verticapy.vDataFrame.avg` method without specifying columns, all numerical vDataFrame columns are included in the operation: +If we run the :py:func:`~verticapy.vDataFrame.avg` method without specifying columns, all numerical :py:mod:`~verticapy.vDataFrame` columns are included in the operation: -.. note:: To examine the generated SQL for each command, turn on the "sql_on" option using :py:func:`~verticapy.set_option`. +.. note:: To examine the generated SQL for each command, turn on the ``sql_on`` option using :py:func:`~verticapy.set_option`. .. ipython:: python @@ -126,10 +126,10 @@ You can then use this truncated list of columns in another method call; for inst Save the current relation -------------------------- -The :py:mod:`~verticapy.vDataFrame` works like a ``view``, a stored query that encapsulates one or more SELECT statements. +The :py:mod:`~verticapy.vDataFrame` works like a ``view``, a stored query that encapsulates one or more ``SELECT`` statements. If the generated relation uses many different functions, the computation time for each method call is greatly increased. -Small transformations don't drastically slow down computation, but heavy transformations (multiple joins, frequent use of advanced analytical funcions, moving windows, etc.) can result in noticeable slowdown. When performing computationally expensive operations, you can aid performance by saving the vDataFrame structure as a table in the Vertica database. We will demonstrate this process in the following example. +Small transformations don't drastically slow down computation, but heavy transformations (multiple joins, frequent use of advanced analytical funcions, moving windows, etc.) can result in noticeable slowdown. When performing computationally expensive operations, you can aid performance by saving the :py:mod:`~verticapy.vDataFrame` structure as a table in the Vertica database. We will demonstrate this process in the following example. First, create a :py:mod:`~verticapy.vDataFrame`, then perform some operations on that :py:mod:`~verticapy.vDataFrame`: @@ -160,7 +160,7 @@ To understand how Vertica executes the different aggregations in the above relat print(titanic.explain()) -Looking at the plan and its associated relation, it's clear that the transformations we applied to the vDataFrame result in a complicated relation. +Looking at the plan and its associated relation, it's clear that the transformations we applied to the :py:mod:`~verticapy.vDataFrame` result in a complicated relation. Each method call to the :py:mod:`~verticapy.vDataFrame` must use this relation for computation. @@ -220,7 +220,7 @@ To demonstrate, create a database connection: vp.connect("VerticaDSN") -When you are done making changes, close the connection with the :func:`~verticapy.close_connection` function: +When you are done making changes, close the connection with the :func:`~verticapy.connection.connect.close_connection` function: .. code-block:: python @@ -321,7 +321,7 @@ For example, if we are only interested in analyzing Titanic passengers who didn' .. raw:: html :file: SPHINX_DIRECTORY/figures/user_guide_introduction_best_practices_filter.html -To drop unnecessary columns from your vDataFrame, use the :py:func:`~verticapy.vDataFrame.drop` method: +To drop unnecessary columns from your :py:mod:`~verticapy.vDataFrame`, use the :py:func:`~verticapy.vDataFrame.drop` method: .. code-block:: python @@ -412,7 +412,7 @@ First, let's send a single query to compute the average for all columns in the : display(vdf.avg(ncols_block = 20)) -We see that there was one SELECT query for all columns in the :py:mod:`~verticapy.vDataFrame`. +We see that there was one ``SELECT`` query for all columns in the :py:mod:`~verticapy.vDataFrame`. You can reduce the impact on the system by using the ``ncols_block`` parameter to split the computation into multiple iterative queries, where the value of the parameter is the number of columns included in each query. For example, setting ``ncols_block`` to 5 will split the computation, which consists of 20 total columns, into 4 separate queries, each of which computes the average for 5 columns: diff --git a/docs/source/user_guide_introduction_installation.rst b/docs/source/user_guide_introduction_installation.rst index 5852ab002..6d0fcbd64 100644 --- a/docs/source/user_guide_introduction_installation.rst +++ b/docs/source/user_guide_introduction_installation.rst @@ -20,7 +20,7 @@ see :ref:`getting_started`. Connect to a DB ---------------- -To connect to a database for the first time, use the :py:func:`~verticapy.new_connection` function, replacing the configuration values with the credentials for your database: +To connect to a database for the first time, use the :py:func:`~verticapy.connection.write.new_connection` function, replacing the configuration values with the credentials for your database: .. code-block:: python @@ -43,13 +43,13 @@ To connect to a database for the first time, use the :py:func:`~verticapy.new_co import verticapy as vp The connection is saved to the VerticaPy connection file under the name specified in the name parameter. To reconnect to -the database using this connection, run the :py:func:`~verticapy.connect` function with the name of the connection as the argument value: +the database using this connection, run the :py:func:`~verticapy.connection.connect.connect` function with the name of the connection as the argument value: .. code-block:: python vp.connect("Vertica_Connection") -To view all available connections, use :py:func:`~verticapy.available_connection`. +To view all available connections, use :py:func:`~verticapy.connection.read.available_connection`. .. code-block:: python @@ -59,16 +59,31 @@ To view all available connections, use :py:func:`~verticapy.available_connection If you need to confirm the parameters for a given function, you can also use the help function: + +.. ipython:: python + :suppress: + + import inspect + import re + + def help(obj): + signature = f"Help on function {obj.__name__} in module {obj.__module__}:\n\n{obj.__name__}{inspect.signature(obj)}" + doc = inspect.getdoc(obj) + if doc: + short_doc = re.split(r"\n\s*Examples\s*[-=]*\s*\n", doc)[0] + print(f"{signature}\n\n{short_doc}") + + .. ipython:: python help(vp.new_connection) -For an interactive start guide, you can use the :py:func:`~verticapy.help_start()` function: +For an interactive start guide, you can use the :py:func:`~verticapy._help.help_start` function: .. code-block:: python vp.help_start() -.. image:: ../../docs/source/_static/website/user_guides/introduction/user_guide_installation_help_start.PNG +.. image:: ../../docs/source/_static/website/user_guides/introduction/user_guide_installation_help_start.png :width: 50% :align: center \ No newline at end of file diff --git a/docs/source/user_guide_introduction_vdf.rst b/docs/source/user_guide_introduction_vdf.rst index 6ebad4666..6b9230017 100644 --- a/docs/source/user_guide_introduction_vdf.rst +++ b/docs/source/user_guide_introduction_vdf.rst @@ -3,10 +3,9 @@ The Virtual DataFrame ===================== -The Virtual DataFrame (vDataFrame) is the core object of the VerticaPy library. Leveraging the power of Vertica and the flexibility of Python, the :py:mod:`~verticapy.vDataFrame` is a Python object that lets you manipulate the data representation in a Vertica database without modifying the underlying data. The data represented by a :py:mod:`~verticapy.vDataFrame` remains in the Vertica database, bypassing the limitations of working memory. When a :py:mod:`~verticapy.vDataFrame` is created or altered, VerticaPy formulates the operation as an SQL query and pushes the computation to the Vertica database, harnessing Vertica's massive parallel processing and in-built functions. Vertica then aggregates and returns the result to VerticaPy. In essence, vDataFrames behave similar to `views `_ in the Vertica database. +The Virtual DataFrame (:py:mod:`~verticapy.vDataFrame`) is the core object of the VerticaPy library. Leveraging the power of Vertica and the flexibility of Python, the :py:mod:`~verticapy.vDataFrame` is a Python object that lets you manipulate the data representation in a Vertica database without modifying the underlying data. The data represented by a :py:mod:`~verticapy.vDataFrame` remains in the Vertica database, bypassing the limitations of working memory. When a :py:mod:`~verticapy.vDataFrame` is created or altered, VerticaPy formulates the operation as an SQL query and pushes the computation to the Vertica database, harnessing Vertica's massive parallel processing and in-built functions. Vertica then aggregates and returns the result to VerticaPy. In essence, vDataFrames behave similar to `views `_ in the Vertica database. -For more information about Vertica's performance advantages, including its columnar orientation and parallelization across -nodes, see the `Vertica documentation `_. +For more information about Vertica's performance advantages, including its columnar orientation and parallelization across nodes, see the `Vertica documentation `_. In the following tutorial, we will introduce the basic functionality of the :py:mod:`~verticapy.vDataFrame` and then explore the ways in which they utilize in-database processing to enhance performance. @@ -73,31 +72,27 @@ First, we download the `Expedia dataset `_. @@ -84,6 +84,6 @@ We have many metrics to evaluate the model. .. raw:: html :file: SPHINX_DIRECTORY/figures/ug_ml_table_regression_2.html -Our example forgoes splitting the data into training and testing, which is important for real-world work. Our main goal in this lesson is to look at the metrics used to evaluate regressions. The most famous metric is ``R2``: generally speaking, the closer ``R2`` is to 1, the better the model is. +Our example forgoes splitting the data into training and testing, which is important for real-world work. Our main goal in this lesson is to look at the metrics used to evaluate regressions. The most famous metric is ``R2``: generally speaking, the closer ``R2`` is to ``1``, the better the model is. In the next lesson, we'll go over :ref:`user_guide.machine_learning.classification` \ No newline at end of file diff --git a/docs/source/user_guide_machine_learning_time_series.rst b/docs/source/user_guide_machine_learning_time_series.rst index 831d76daf..ae8c98fb2 100644 --- a/docs/source/user_guide_machine_learning_time_series.rst +++ b/docs/source/user_guide_machine_learning_time_series.rst @@ -4,7 +4,7 @@ Time Series ============ -Time series models are a type of regression on a dataset with a timestamp label. +Time series models are a type of regression on a dataset with a ``timestamp`` label. The following example creates a time series model to predict the number of forest fires in Brazil with the ``amazon`` dataset. diff --git a/docs/source/user_guide_performance_qprof.rst b/docs/source/user_guide_performance_qprof.rst index ddf285a9a..92e2c827d 100644 --- a/docs/source/user_guide_performance_qprof.rst +++ b/docs/source/user_guide_performance_qprof.rst @@ -9,8 +9,8 @@ This starter notebook will help you get up and running with the Query Profiler ( This tool is a work in progress and the VerticaPy team is continuously adding new features. See also :py:mod:`~verticapy.performance.vertica.qprof.QueryProfiler` , -:py:func:`~verticapy.performance.vertica.qprof_interface.QueryProfilerInterface` , -:py:func:`~verticapy.performance.vertica.qprof_interface.QueryProfilerComparison` . +:py:mod:`~verticapy.performance.vertica.qprof_interface.QueryProfilerInterface` , +:py:mod:`~verticapy.performance.vertica.qprof_interface.QueryProfilerComparison` . VerticaPyLab ------------- @@ -213,7 +213,7 @@ Once the :py:mod:`~verticapy.performance.vertica.qprof.QueryProfiler` object is To visualize the query plan, run :py:func:`verticapy.QueryProfilerInterface.get_qplan_tree`, which is customizable, allowing you to specify certain metrics or focus on a specified tree path: -.. image:: /source/_static/website/user_guides/performance/user_guide_performance_qprof_get_qplan_tree.PNG +.. image:: /source/_static/website/user_guides/performance/user_guide_performance_qprof_get_qplan_tree.png :width: 80% :align: center @@ -290,7 +290,7 @@ To export a :py:mod:`~verticapy.performance.vertica.qprof.QueryProfiler` object, Import +++++++ -To import a :py:mod:`~verticapy.performance.vertica.qprof.QueryProfiler` object, use the :py:func:`~verticapy.performance.vertica.QueryProfiler.import_profile` method and provide the ``target_schema`` and ``key_id``. +To import a :py:mod:`~verticapy.performance.vertica.qprof.QueryProfiler` object, use the :py:func:`~verticapy.performance.vertica.qprof.QueryProfiler.import_profile` method and provide the ``target_schema`` and ``key_id``. Make sure the ``key_id`` is unique/unused. Let us create a new schema to load this into: @@ -453,7 +453,7 @@ To view the cpu time of the query in a bar graph: :suppress: :okwarning: - fig = qprof.get_cpu_time(kind="bar") + fig = qprof.get_cpu_time(kind="bar", width = 800) fig.write_html("SPHINX_DIRECTORY/figures/user_guides_performance_qprof_cpu_bar.html") .. raw:: html diff --git a/docs/source/vertica_documentation.rst b/docs/source/vertica_documentation.rst index 629ec19d0..77d0fa1bd 100644 --- a/docs/source/vertica_documentation.rst +++ b/docs/source/vertica_documentation.rst @@ -17,7 +17,7 @@ Vertica Documentation VerticaPy
- Documentation 1.0.x + Documentation 1.1.x
Below are the links for relevant ``Vertica`` documentation: diff --git a/docs/source/whats_new_v1_1_0.rst b/docs/source/whats_new_v1_1_0.rst index d15d6555d..634ed6fa9 100644 --- a/docs/source/whats_new_v1_1_0.rst +++ b/docs/source/whats_new_v1_1_0.rst @@ -14,6 +14,18 @@ Options - ``max_cellwidth``: Maximum width of any VerticaPy table's cell. - ``max_tableheight``: Maximum height of VerticaPy tables. - ``theme``: Theme used to display the VerticaPy objects ('light', 'dark' or 'sphinx'). + - ``verbosity``: This will set the amount of information to display while executing VerticaPy functions. + +.. note:: + + There are four levels of ``verbosity``: + + - 0 (silent) + - 1 (warning) + - 2 (info) + - 3 (debug) + +_________ Machine Learning ----------------- @@ -30,6 +42,29 @@ in utilizing PLS regression. For more details, please look at :py:mod:`~verticapy.machine_learning.vertica.linear_model.PLSRegression` +Example use: + +.. code-block:: python + + from verticapy.machine_learning.vertica import PLSRegression + + model = PLSRegression() + model.fit( + train_data, + [ + "col1", + "col2", + "col3", + ], + "Y", + test_data, + ) + +.. note:: + + All the ML models that were introduced in version 1.0.x are also supported in version 1.1.x. +___________ + Performance ------------ @@ -84,10 +119,18 @@ QueryProfilerComparison qprof_compare.get_qplan_tree() +.. image:: ../../docs/source/_static/whats_new_1_1_0_qprof_get_qplan_tree.png + :width: 80% + :align: center + +___________ + Deprecations ------------- -Because of the re-structuring of VerticaPy, some parts of the heirarchy has changed. +.. warning:: + + Because of the re-structuring of VerticaPy, some previous imports will not work. Please look at the latest doc for the up-to-date imports. diff --git a/verticapy/__init__.py b/verticapy/__init__.py index be8957af0..0297c0e18 100755 --- a/verticapy/__init__.py +++ b/verticapy/__init__.py @@ -36,10 +36,10 @@ ) __url__: str = "https://github.com/vertica/verticapy/" __license__: str = "Apache License, Version 2.0" -__version__: str = "1.1.0-beta" +__version__: str = "1.1.0" __iteration__: int = 1 -__date__: str = "10282024" -__last_commit__: str = "4bad461854b686fedcfb56dcedfa2546bad7ab7d" +__date__: str = "10292024" +__last_commit__: str = "00ea3d3a8696d23b93c59771492887c6e2d6a48b" __long_version__: str = f"{__version__}-{__iteration__}—{__date__}-{__last_commit__}" __codecov__: float = 0.84 diff --git a/verticapy/_utils/_sql/_vertica_version.py b/verticapy/_utils/_sql/_vertica_version.py index 25ff20e79..8af315056 100755 --- a/verticapy/_utils/_sql/_vertica_version.py +++ b/verticapy/_utils/_sql/_vertica_version.py @@ -61,6 +61,10 @@ "PMMLModel": [10, 0, 0], "PoissonRegressor": [12, 0, 0], "prc_curve": [9, 1, 0], + "QueryProfiler": [11, 0, 0], + "QueryProfilerComparison": [11, 0, 0], + "QueryProfilerInterface": [11, 0, 0], + "QueryProfilerStats": [11, 0, 0], "RandomForestClassifier": [8, 1, 1], "RandomForestRegressor": [9, 0, 1], "read_file": [11, 1, 1], diff --git a/verticapy/connection/utils.py b/verticapy/connection/utils.py index 845039649..c07c24718 100755 --- a/verticapy/connection/utils.py +++ b/verticapy/connection/utils.py @@ -101,7 +101,9 @@ def get_connection_file() -> str: | :py:func:`~verticapy.connection.new_connection` : Creates a new VerticaPy connection. """ - if "VERTICAPY_CONNECTION" in os.environ: + if "VERTICAPY_CONNECTION" in os.environ and os.path.exists( + os.environ["VERTICAPY_CONNECTION"] + ): return os.environ["VERTICAPY_CONNECTION"] path = os.path.join(os.path.expanduser("~"), ".vertica") os.makedirs(path, 0o700, exist_ok=True) diff --git a/verticapy/core/vdataframe/_corr.py b/verticapy/core/vdataframe/_corr.py index d8fc72927..17ad2d057 100755 --- a/verticapy/core/vdataframe/_corr.py +++ b/verticapy/core/vdataframe/_corr.py @@ -875,10 +875,10 @@ def corr( } ) fig = data.corr(method = "pearson") - fig.write_html("/project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_corr_matrix.html") + fig.write_html("SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_corr_matrix.html") .. raw:: html - :file: /project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_corr_matrix.html + :file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_corr_matrix.html You can also use the parameter focus to only compute a correlation vector. @@ -890,10 +890,10 @@ def corr( :suppress: fig = data.corr(method = "pearson", focus = "score1") - fig.write_html("/project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_corr_vector.html") + fig.write_html("SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_corr_vector.html") .. raw:: html - :file: /project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_corr_vector.html + :file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_corr_vector.html It is less expensive and it allows you to focus your search on one specific column. @@ -1258,10 +1258,10 @@ def cov( } ) fig = data.cov() - fig.write_html("/project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_cov_matrix.html") + fig.write_html("SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_cov_matrix.html") .. raw:: html - :file: /project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_cov_matrix.html + :file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_cov_matrix.html You can also use the parameter focus to only compute a covariance vector. @@ -1273,10 +1273,10 @@ def cov( :suppress: fig = data.cov(method = "pearson", focus = "score1") - fig.write_html("/project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_cov_vector.html") + fig.write_html("SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_cov_vector.html") .. raw:: html - :file: /project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_cov_vector.html + :file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_cov_vector.html It is less expensive and it allows you to focus your search on one specific column. @@ -1436,10 +1436,10 @@ def regr( } ) fig = data.regr(method = "beta") - fig.write_html("/project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_regr_beta_matrix.html") + fig.write_html("SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_regr_beta_matrix.html") .. raw:: html - :file: /project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_regr_beta_matrix.html + :file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_regr_beta_matrix.html Draw the regression matrix using the Alpha coefficient. @@ -1463,10 +1463,10 @@ def regr( } ) fig = data.regr(method = "alpha") - fig.write_html("/project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_regr_alpha_matrix.html") + fig.write_html("SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_regr_alpha_matrix.html") .. raw:: html - :file: /project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_regr_alpha_matrix.html + :file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_regr_alpha_matrix.html Draw the regression matrix using the R2 correlation coefficient. @@ -1490,10 +1490,10 @@ def regr( } ) fig = data.regr(method = "r2") - fig.write_html("/project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_regr_r2_matrix.html") + fig.write_html("SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_regr_r2_matrix.html") .. raw:: html - :file: /project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_regr_r2_matrix.html + :file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_regr_r2_matrix.html For more examples, please look at the :ref:`chart_gallery.corr` page of the :ref:`chart_gallery`. Those ones are related to correlation matrix, but the @@ -1743,7 +1743,7 @@ def acf( data = load_amazon() .. raw:: html - :file: /project/data/VerticaPy/docs/figures/datasets_loaders_load_amazon.html + :file: SPHINX_DIRECTORY/figures/datasets_loaders_load_amazon.html Draw the ACF Plot. @@ -1773,10 +1773,10 @@ def acf( width = 600, height = 400, ) - fig.write_html("/project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_acf_plot.html") + fig.write_html("SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_acf_plot.html") .. raw:: html - :file: /project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_acf_plot.html + :file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_acf_plot.html For more examples, please look at the :ref:`chart_gallery.acf` page of the :ref:`chart_gallery`. @@ -1977,7 +1977,7 @@ def pacf( data = load_amazon() .. raw:: html - :file: /project/data/VerticaPy/docs/figures/datasets_loaders_load_amazon.html + :file: SPHINX_DIRECTORY/figures/datasets_loaders_load_amazon.html Draw the PACF Plot. @@ -2007,10 +2007,10 @@ def pacf( width = 600, height = 450, ) - fig.write_html("/project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_pacf_plot.html") + fig.write_html("SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_pacf_plot.html") .. raw:: html - :file: /project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_pacf_plot.html + :file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_pacf_plot.html For more examples, please look at the :ref:`chart_gallery.acf` page of the :ref:`chart_gallery`. Those ones are related to ACF plots, but the customization @@ -2171,7 +2171,7 @@ def iv_woe( data = load_titanic() .. raw:: html - :file: /project/data/VerticaPy/docs/figures/datasets_loaders_load_titanic.html + :file: SPHINX_DIRECTORY/figures/datasets_loaders_load_titanic.html Draw the IV Bar chart. @@ -2187,10 +2187,10 @@ def iv_woe( vp.set_option("plotting_lib", "plotly") data = load_titanic() fig = data.iv_woe(y = "survived", nbins = 20) - fig.write_html("/project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_iv_woe_plot.html") + fig.write_html("SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_iv_woe_plot.html") .. raw:: html - :file: /project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_iv_woe_plot.html + :file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_iv_woe_plot.html .. hint:: @@ -2281,7 +2281,7 @@ def iv_woe(self, y: str, nbins: int = 10) -> TableSample: data = load_titanic() .. raw:: html - :file: /project/data/VerticaPy/docs/figures/datasets_loaders_load_titanic.html + :file: SPHINX_DIRECTORY/figures/datasets_loaders_load_titanic.html Draw the IV Bar chart. @@ -2294,12 +2294,12 @@ def iv_woe(self, y: str, nbins: int = 10) -> TableSample: from verticapy.datasets import load_titanic data = load_titanic() - html_file = open("/project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_iv_woe_table.html", "w") + html_file = open("SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_iv_woe_table.html", "w") html_file.write(data["age"].iv_woe(y = "survived", nbins = 20)._repr_html_()) html_file.close() .. raw:: html - :file: /project/data/VerticaPy/docs/figures/core_vDataFrame_vDFCorr_iv_woe_table.html + :file: SPHINX_DIRECTORY/figures/core_vDataFrame_vDFCorr_iv_woe_table.html .. hint:: diff --git a/verticapy/core/vdataframe/_filter.py b/verticapy/core/vdataframe/_filter.py index 1cc1528d4..d72dfc814 100755 --- a/verticapy/core/vdataframe/_filter.py +++ b/verticapy/core/vdataframe/_filter.py @@ -15,7 +15,7 @@ permissions and limitations under the License. """ import copy -import random +import secrets from typing import Literal, Optional, Union, TYPE_CHECKING from collections.abc import Iterable @@ -850,7 +850,7 @@ def drop_duplicates(self, columns: Optional[SQLColumns] = None) -> "vDataFrame": ) name = ( "__verticapy_duplicated_index__" - + str(random.randint(0, 10000000)) + + str(secrets.randbelow(10000001)) + "_" ) self.eval( @@ -1760,14 +1760,14 @@ def sample( ) by = format_type(by, dtype=list) by = self.format_colnames(by) - random_int = random.randint(0, 10000000) + random_int = secrets.randbelow(10000001) name = f"__verticapy_random_{random_int}__" name2 = f"__verticapy_random_{random_int + 1}__" vdf = self.copy() assert 0 < x < 1, ValueError("Parameter 'x' must be between 0 and 1") if method == "random": random_state = conf.get_option("random_state") - random_seed = random.randint(int(-10e6), int(10e6)) + random_seed = secrets.randbelow(2000001) - 1000000 if isinstance(random_state, int): random_seed = random_state random_func = _seeded_random_function(random_seed) diff --git a/verticapy/core/vdataframe/_machine_learning.py b/verticapy/core/vdataframe/_machine_learning.py index ad3cb97ce..346751f53 100755 --- a/verticapy/core/vdataframe/_machine_learning.py +++ b/verticapy/core/vdataframe/_machine_learning.py @@ -15,7 +15,7 @@ permissions and limitations under the License. """ import math -import random +import secrets from itertools import combinations_with_replacement from typing import Literal, Optional, Union, TYPE_CHECKING @@ -2090,7 +2090,7 @@ def train_test_split( random_seed = ( random_state if isinstance(random_state, int) - else random.randint(int(-10e6), int(10e6)) + else secrets.randbelow(2000001) - 1000000 ) random_func = _seeded_random_function(random_seed) q = _executeSQL( diff --git a/verticapy/core/vdataframe/_math.py b/verticapy/core/vdataframe/_math.py index c6399adc8..28ce2c37e 100755 --- a/verticapy/core/vdataframe/_math.py +++ b/verticapy/core/vdataframe/_math.py @@ -15,8 +15,8 @@ permissions and limitations under the License. """ import copy -import random import re +import secrets from typing import Literal, Optional, Union, TYPE_CHECKING from verticapy._typing import PythonNumber, PythonScalar, SQLColumns @@ -425,7 +425,7 @@ def analytic( f"when using analytic method '{func}'" ) if func in ("skewness", "kurtosis", "aad", "mad", "jb"): - random_nb = random.randint(0, 10000000) + random_nb = secrets.randbelow(10000001) column_str = columns[0].replace('"', "") mean_name = f"{column_str}_mean_{random_nb}" median_name = f"{column_str}_median_{random_nb}" diff --git a/verticapy/core/vdataframe/_rolling.py b/verticapy/core/vdataframe/_rolling.py index 31182ea42..fa1897463 100755 --- a/verticapy/core/vdataframe/_rolling.py +++ b/verticapy/core/vdataframe/_rolling.py @@ -15,7 +15,7 @@ permissions and limitations under the License. """ import datetime -import random +import secrets from typing import Optional, Union from verticapy._typing import SQLColumns, TYPE_CHECKING @@ -289,7 +289,7 @@ def rolling( if func in ("kurtosis", "skewness", "aad", "prod", "jb"): if func in ("skewness", "kurtosis", "aad", "jb"): columns_0_str = columns[0].replace('"', "").lower() - random_int = random.randint(0, 10000000) + random_int = secrets.randbelow(10000001) mean_name = f"{columns_0_str}_mean_{random_int}" std_name = f"{columns_0_str}_std_{random_int}" count_name = f"{columns_0_str}_count_{random_int}" diff --git a/verticapy/machine_learning/model_selection/model_validation.py b/verticapy/machine_learning/model_selection/model_validation.py index e1746aa85..3477924c1 100755 --- a/verticapy/machine_learning/model_selection/model_validation.py +++ b/verticapy/machine_learning/model_selection/model_validation.py @@ -15,7 +15,7 @@ permissions and limitations under the License. """ import copy -import random +import secrets import time from typing import Literal, Optional, Union @@ -557,7 +557,7 @@ def cross_validate( estimator.drop() random_state = conf.get_option("random_state") random_state = ( - random.randint(int(-10e6), int(10e6)) + secrets.randbelow(2000001) - 1000000 if not random_state else random_state + i ) diff --git a/verticapy/machine_learning/vertica/cluster.py b/verticapy/machine_learning/vertica/cluster.py index 6207c8c42..09abaeb46 100755 --- a/verticapy/machine_learning/vertica/cluster.py +++ b/verticapy/machine_learning/vertica/cluster.py @@ -1311,14 +1311,14 @@ class KPrototypes(KMeans): :suppress: :okexcept: - result = model.predict(data, ["density", "sulphates"], name = "Cluster IDs") + result = model.predict(data, ["color", "sulphates"], name = "Cluster IDs") html_file = open("SPHINX_DIRECTORY/figures/machine_learning_vertica_kprototypes_prediction.html", "w") html_file.write(result._repr_html_()) html_file.close() .. code-block:: python - model.predict(data, ["density", "sulphates"], name = "Cluster IDs") + model.predict(data, ["color", "sulphates"], name = "Cluster IDs") .. raw:: html :file: SPHINX_DIRECTORY/figures/machine_learning_vertica_kprototypes_prediction.html diff --git a/verticapy/machine_learning/vertica/ensemble.py b/verticapy/machine_learning/vertica/ensemble.py index d16df633d..90b153428 100755 --- a/verticapy/machine_learning/vertica/ensemble.py +++ b/verticapy/machine_learning/vertica/ensemble.py @@ -15,7 +15,7 @@ permissions and limitations under the License. """ -import random +import secrets from typing import Literal, Optional, Union import numpy as np @@ -140,7 +140,7 @@ def _to_json_tree_dict(self, tree_id: int, c: str = None) -> dict: n_nodes = len(attributes[0]) split_conditions = [] parents = [0 for i in range(n_nodes)] - parents[0] = random.randint(n_nodes + 1, 999999999) + parents[0] = min(secrets.randbelow(1000000000) + n_nodes + 1, 999999999) for i in range(n_nodes): left_child = attributes[0][i] right_child = attributes[1][i] diff --git a/verticapy/machine_learning/vertica/tsa/ensemble.py b/verticapy/machine_learning/vertica/tsa/ensemble.py index 822bc143f..79be67343 100755 --- a/verticapy/machine_learning/vertica/tsa/ensemble.py +++ b/verticapy/machine_learning/vertica/tsa/ensemble.py @@ -106,7 +106,7 @@ class TimeSeriesByCategory(TimeSeriesModelBase): amazon_full = vpd.load_amazon() .. raw:: html - :file: /project/data/VerticaPy/docs/figures/datasets_loaders_load_amazon.html + :file: SPHINX_DIRECTORY/figures/datasets_loaders_load_amazon.html .. ipython:: python :suppress: @@ -182,10 +182,10 @@ class TimeSeriesByCategory(TimeSeriesModelBase): vp.set_option("plotting_lib", "plotly") fig = model.plot(idx = 0, npredictions = 5) - fig.write_html("/project/data/VerticaPy/docs/figures/machine_learning_vertica_tsa_ensemble_timeseriesbycategory_1.html") + fig.write_html("SPHINX_DIRECTORY/figures/machine_learning_vertica_tsa_ensemble_timeseriesbycategory_1.html") .. raw:: html - :file: /project/data/VerticaPy/docs/figures/machine_learning_vertica_tsa_ensemble_timeseriesbycategory_1.html + :file: SPHINX_DIRECTORY/figures/machine_learning_vertica_tsa_ensemble_timeseriesbycategory_1.html .. note:: diff --git a/verticapy/performance/vertica/qprof.py b/verticapy/performance/vertica/qprof.py index 8ef6f44ab..91f54cf4a 100755 --- a/verticapy/performance/vertica/qprof.py +++ b/verticapy/performance/vertica/qprof.py @@ -34,7 +34,10 @@ from verticapy._utils._sql._collect import save_verticapy_logs from verticapy._utils._sql._format import clean_query, format_query, format_type from verticapy._utils._sql._sys import _executeSQL -from verticapy._utils._sql._vertica_version import vertica_version +from verticapy._utils._sql._vertica_version import ( + check_minimum_version, + vertica_version, +) from verticapy.performance.vertica.collection.profile_export import ProfileExport from verticapy.performance.vertica.collection.profile_import import ProfileImport @@ -51,14 +54,6 @@ class QueryProfiler: """ Base class to profile queries. - .. important:: - - This class is currently under development - and remains in beta. Please note that the - parameters may undergo changes in the future. - We plan to introduce a stable version in - VerticaPy 1.0.3 or later. - .. important:: Most of the classes are not available in @@ -973,6 +968,7 @@ class QueryProfiler: # Init Functions + @check_minimum_version @save_verticapy_logs def __init__( self, diff --git a/verticapy/performance/vertica/qprof_interface.py b/verticapy/performance/vertica/qprof_interface.py index 13a371687..0ba714144 100644 --- a/verticapy/performance/vertica/qprof_interface.py +++ b/verticapy/performance/vertica/qprof_interface.py @@ -20,7 +20,9 @@ from typing import Optional, Union import verticapy._config.config as conf +from verticapy._utils._sql._collect import save_verticapy_logs from verticapy._utils._print import print_message +from verticapy._utils._sql._vertica_version import check_minimum_version from verticapy.jupyter._javascript import read_package_file, replace_value from verticapy.jupyter._widgets import Visualizer, Item, makeItems @@ -39,6 +41,8 @@ class QueryProfilerInterface(QueryProfilerStats): features. """ + @check_minimum_version + @save_verticapy_logs def __init__( self, transactions: Union[None, str, list[int], list[tuple[int, int]]] = None, @@ -996,13 +1000,19 @@ def get_step_funcs(self): class QueryProfilerComparison: """ - Initializes a QueryProfilerComparison object with two QueryProfilerInterface instances for side-by-side comparison. + Initializes a ``QueryProfilerComparison`` object with + two ``QueryProfilerInterface`` instances for side-by-side + comparison. Parameters: - qprof1 (QueryProfilerInterface): The first QueryProfilerInterface instance for comparison. - qprof2 (QueryProfilerInterface): The second QueryProfilerInterface instance for comparison. + - qprof1 (``QueryProfilerInterface``): The first + ``QueryProfilerInterface`` instance for comparison. + - qprof2 (``QueryProfilerInterface``): The second + ``QueryProfilerInterface`` instance for comparison. """ + @check_minimum_version + @save_verticapy_logs def __init__(self, qprof1, qprof2): self.qprof1 = qprof1 self.qprof2 = qprof2 @@ -1053,7 +1063,7 @@ def on_metric_toggle_change(change): def _create_qprof1_controls(self): """ - Creates interactive controls for qprof1. + Creates interactive controls for ``qprof1``. """ interactive_output = self.qprof1._interactive_output return widgets.HBox( @@ -1063,7 +1073,7 @@ def _create_qprof1_controls(self): def _create_qprof2_controls(self): """ - Creates interactive controls for qprof2. + Creates interactive controls for ``qprof2``. """ interactive_output = self.qprof2._interactive_output return widgets.HBox( @@ -1073,7 +1083,8 @@ def _create_qprof2_controls(self): def _create_controls(self): """ - Creates side-by-side controls for both qprof1 and qprof2. + Creates side-by-side controls for + both ``qprof1`` and ``qprof2``. """ q1_control = self.qprof1._accordions q1_control.layout.width = "50%" @@ -1094,13 +1105,16 @@ def _create_controls(self): def _sync_accordion_selection(self): """ - Synchronizes the accordion selection of qprof1 and qprof2. - When an accordion is selected in qprof1, it automatically updates the selection in qprof2. + Synchronizes the accordion selection of ``qprof1`` + and ``qprof2``. When an accordion is selected in + ``qprof1``, it automatically updates the selection + in ``qprof2``. """ def on_accordion_change(change): """ - Callback function to update qprof2's accordion selection when qprof1's accordion selection changes. + Callback function to update ``qprof2`` accordion + selection when ``qprof1`` accordion selection changes. """ if change["name"] == "selected_index": self.qprof2._accordions.selected_index = change["new"] @@ -1110,12 +1124,13 @@ def on_accordion_change(change): def _sync_metric_values(self, switch): """ - Sync the metric tab values for both qprof objects + Sync the metric tab values for both ``qprof`` objects """ def on_metric_dropdown1_change(change): """ - Callback function to update qprof2's dropdown selection when qprof1's dropdown selection changes. + Callback function to update ``qprof2`` dropdown + selection when ``qprof1`` dropdown selection changes. """ val = change["new"] self.qprof2._accordions.children[0].children[0].value = val @@ -1148,7 +1163,9 @@ def on_metric_dropdown1_change(change): def on_metric_dropdown2_change(change): """ - Callback function to update qprof2's dropdown selection when qprof1's dropdown selection changes. + Callback function to update ``qprof2`` + dropdown selection when ``qprof1`` + dropdown selection changes. """ val = change["new"] self.qprof2._accordions.children[0].children[1].value = val @@ -1189,8 +1206,8 @@ def sync_checkboxes(checkbox1, checkbox2): Syncs the values of two checkboxes. Parameters: - checkbox1 (Checkbox): The first checkbox widget. - checkbox2 (Checkbox): The second checkbox widget. + - ``checkbox1 ``(Checkbox): The first checkbox widget. + - ``checkbox2`` (Checkbox): The second checkbox widget. """ def on_checkbox_change(change): @@ -1217,7 +1234,8 @@ def on_checkbox_change(change): def unsync_all_checkboxes(self): """ - Unbind synchronization of checkboxes between qprof1 and qprof2. + Unbind synchronization of checkboxes + between ``qprof1`` and ``qprof2``. """ def unsync_checkboxes(checkbox1): @@ -1243,10 +1261,8 @@ def unsync_checkboxes(checkbox1): unsync_checkboxes(checkbox_op_q1) unsync_checkboxes(checkbox_desc_q1) - def display(self): + def get_qplan_tree(self): """ Displays the final side-by-side UI. """ print_message(self.side_by_side_ui, "display") - - get_qplan_tree = display diff --git a/verticapy/plotting/_highcharts/machine_learning/svm.py b/verticapy/plotting/_highcharts/machine_learning/svm.py index ef84f4813..533d05bbf 100755 --- a/verticapy/plotting/_highcharts/machine_learning/svm.py +++ b/verticapy/plotting/_highcharts/machine_learning/svm.py @@ -14,7 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. """ -import random +import secrets from typing import Literal, Optional import numpy as np @@ -105,8 +105,12 @@ def draw(self, chart: Optional[HChart] = None, **style_kwargs) -> HChart: [-self.data["coef"][0] / self.data["coef"][1], i / 100] for i in range(-100, 101) ] - data_1 = [[xi, 2 * (random.random() - 0.5)] for xi in x1] - data_0 = [[xi, 2 * (random.random() - 0.5)] for xi in x0] + data_1 = [ + [xi, 2 * (secrets.randbelow(10000001) / 10000000 - 0.5)] for xi in x1 + ] + data_0 = [ + [xi, 2 * (secrets.randbelow(10000001) / 10000000 - 0.5)] for xi in x0 + ] elif len(self.layout["columns"]) == 3: y = self.data["X"][:, 1] y0, y1 = y[w == 0], y[w == 1] diff --git a/verticapy/plotting/_matplotlib/machine_learning/svm.py b/verticapy/plotting/_matplotlib/machine_learning/svm.py index a83a85333..3ba068298 100755 --- a/verticapy/plotting/_matplotlib/machine_learning/svm.py +++ b/verticapy/plotting/_matplotlib/machine_learning/svm.py @@ -14,7 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. """ -import random +import secrets from typing import Literal, Optional import numpy as np @@ -97,7 +97,10 @@ def draw( all_scatter += [ ax.scatter( x, - [2 * (random.random() - 0.5) for xi in x], + [ + 2 * (secrets.randbelow(10000001) / 10000000 - 0.5) + for xi in x + ], **self._update_dict(s, style_kwargs, i), ) ] diff --git a/verticapy/plotting/base.py b/verticapy/plotting/base.py index d48059b8a..5e8a6a51a 100755 --- a/verticapy/plotting/base.py +++ b/verticapy/plotting/base.py @@ -17,6 +17,7 @@ import copy import math import random +import secrets from typing import Callable, Literal, Optional, Union, TYPE_CHECKING import numpy as np @@ -1617,12 +1618,24 @@ def _compute_outliers_params( inliers = x[abs(zs) <= threshold] n = len(inliers) inliers = np.column_stack( - (inliers, [2 * (random.random() - 0.5) for i in range(n)]) + ( + inliers, + [ + 2 * (secrets.randbelow(10000001) / 10000000 - 0.5) + for i in range(n) + ], + ) ) outliers = x[abs(zs) > threshold] n = len(outliers) outliers = np.column_stack( - (outliers, [2 * (random.random() - 0.5) for i in range(n)]) + ( + outliers, + [ + 2 * (secrets.randbelow(10000001) / 10000000 - 0.5) + for i in range(n) + ], + ) ) else: zvals = None

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Teban + :img-top: _static/website/about_us/team/member14.jpg :link: # :text-align: center :class-card: member-pics-card - ML Engineer & Scrum Master + Customer Engineering .. grid-item:: - .. card:: Matteo Monaldi - :img-top: _static/website/about_us/team/member5.jpg + .. card:: George Larionov + :img-top: _static/website/about_us/team/member10.jpg :link: # :text-align: center :class-card: member-pics-card - Lead Data Scientist + ML Engineer & Scrum Master .. grid-item:: - .. card:: Abdelhak Zabour - :img-top: _static/website/about_us/team/member6.jpg + .. card:: Matteo Monaldi + :img-top: _static/website/about_us/team/member8.jpg :link: # :text-align: center :class-card: member-pics-card - Data Scientist + Lead Data Scientist .. grid-item:: - .. card:: Arash Fard - :img-top: _static/website/about_us/team/member7.jpg + .. card:: Abdelhak Zabour + :img-top: _static/website/about_us/team/member9.jpg :link: # :text-align: center :class-card: member-pics-card - Senior ML Engineer + Data Scientist .. grid-item:: .. card:: Roy Paulin - :img-top: _static/website/about_us/team/member8.jpg + :img-top: _static/website/about_us/team/member13.jpg :link: # :text-align: center :class-card: member-pics-card @@ -267,7 +267,7 @@ Team Members .. grid-item:: .. card:: Xiaozhong Zhang - :img-top: _static/website/about_us/team/member9.jpg + :img-top: _static/website/about_us/team/member12.jpg :link: # :text-align: center :class-card: member-pics-card @@ -277,7 +277,7 @@ Team Members .. grid-item:: .. card:: Zachary Nowak - :img-top: _static/website/about_us/team/member10.jpg + :img-top: _static/website/about_us/team/member11.jpg :link: # :text-align: center :class-card: member-pics-card @@ -287,7 +287,7 @@ Team Members .. grid-item:: .. card:: Abhishek Sharma - :img-top: _static/website/about_us/team/member11.jpg + :img-top: _static/website/about_us/team/member4.jpg :link: # :text-align: center :class-card: member-pics-card @@ -297,7 +297,7 @@ Team Members .. grid-item:: .. card:: Vikash Singh - :img-top: _static/website/about_us/team/member12.jpg + :img-top: _static/website/about_us/team/member5.jpg :link: # :text-align: center :class-card: member-pics-card @@ -307,7 +307,7 @@ Team Members .. grid-item:: .. card:: Tyler Consigny - :img-top: _static/website/about_us/team/member13.jpg + :img-top: _static/website/about_us/team/member6.jpg :link: # :text-align: center :class-card: member-pics-card @@ -317,7 +317,7 @@ Team Members .. grid-item:: .. card:: Kevin Xu - :img-top: _static/website/about_us/team/member14.jpg + :img-top: _static/website/about_us/team/member15.jpg :link: # :text-align: center :class-card: member-pics-card @@ -327,7 +327,7 @@ Team Members .. grid-item:: .. card:: Brent Eynon - :img-top: _static/website/about_us/team/member15.jpg + :img-top: _static/website/about_us/team/member7.jpg :link: # :text-align: center :class-card: member-pics-card diff --git a/docs/source/api.rst b/docs/source/api.rst index 0c0a697b6..c2bd0dd04 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -17,7 +17,7 @@ API Reference VerticaPy

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