fixed bug in clustering

examples/clustering/fit.py

@@ -0,0 +1,24 @@
+from igel import Igel
+
+"""
+The goal of igel is to use ML without writing code. Therefore, the right and simplest way to use igel is from terminal.
+You can run ` igel fit -dp path_to_dataset -yml path_to_yaml_file`.
+
+Alternatively, you can write code if you want. This example below demonstrates how to use igel if you want to write code.
+However, I suggest you try and use the igel CLI. Type igel -h in your terminal to know more.
+
+===============================================================================================================
+
+This example fits a machine learning model on the indian-diabetes dataset
+
+- default model here is the neural network and the configuration are provided in neural-network.yaml file
+- You can switch to random forest by providing the random-forest.yaml as the config file in the parameters
+
+"""
+
+mock_fit_params = {'data_path': '../data/clustering-data/train.csv',
+                   'yaml_path': './igel.yaml',
+                   'cmd': 'fit'}
+
+Igel(**mock_fit_params)
+

examples/clustering/igel.yaml

@@ -0,0 +1,18 @@
+
+dataset:
+    type: csv
+
+# model definition
+model:
+    type: clustering
+    algorithm: KMeans
+    arguments:  # if you don't provide these arguments, then default will be used
+        n_clusters: 3
+        init: random
+        n_init: 10
+        max_iter: 300
+        tol: 0.0004
+        random_state: 0
+
+# target you want to predict
+target:     # you can keep this empty since you want to use clustering

examples/clustering/predict.py

@@ -0,0 +1,21 @@
+from igel import Igel
+
+
+"""
+The goal of igel is to use ML without writing code. Therefore, the right and simplest way to use igel is from terminal.
+You can run ` igel predict -dp path_to_dataset`.
+
+Alternatively, you can write code if you want. This example below demonstrates how to use igel if you want to write code.
+However, I suggest you try and use the igel CLI. Type igel -h in your terminal to know more.
+
+
+===============================================================================================================
+
+This example uses the pre-fitted machine learning model to generate predictions
+
+"""
+
+mock_pred_params = {'data_path': '../data/clustering-data/train.csv',
+                    'cmd': 'predict'}
+
+Igel(**mock_pred_params)

examples/data/clustering-data/raw_data.csv

@@ -0,0 +1,151 @@
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examples/data/clustering-data/train.csv

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