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is_selected is not an argument #46

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132 changes: 95 additions & 37 deletions tsod/active_learning/plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,13 @@
"test_outlier": "#fd7c99",
"test_normal": "#0fefc7",
}
MARKER_SIZES = {"selected": 10, "outlier": 12, "normal": 12, "test_outlier": 12, "test_normal": 12}
MARKER_SIZES = {
"selected": 10,
"outlier": 12,
"normal": 12,
"test_outlier": 12,
"test_normal": 12,
}
MARKER_VALUES = {
"selected": "S",
"outlier": "O",
Expand Down Expand Up @@ -46,44 +52,58 @@ def cachable_get_outlier_counts(
) -> pd.DataFrame:
with st.spinner("Creating new distribution plot..."):
state = get_as(dataset_name, series)
dataset: pd.DataFrame = st.session_state["inference_results"][dataset_name][series]
dataset: pd.DataFrame = st.session_state["inference_results"][dataset_name][
series
]

dataset["outlier_group"] = range(len(dataset))
dataset["outlier_group"] = (dataset["outlier_group"] // number_of_datapoints).astype(
np.int16
)
dataset["outlier_group"] = (
dataset["outlier_group"] // number_of_datapoints
).astype(np.int16)

threshold_timestamps = (
dataset.reset_index().groupby("outlier_group")["index"].first().to_list()
)
threshold_timestamps.append(dataset.index.max())
ranges = [f"{i} - {j}" for i, j in zip(threshold_timestamps, threshold_timestamps[1:])]
ranges = [
f"{i} - {j}" for i, j in zip(threshold_timestamps, threshold_timestamps[1:])
]

out_columns = copy.deepcopy(model_names)
for m in model_names:
out_columns.extend([f"{m} Missed Train Outliers", f"{m} Missed Test Outliers"])
out_columns.extend(
[f"{m} Missed Train Outliers", f"{m} Missed Test Outliers"]
)
out_columns.extend(["Marked Train Outliers", "Marked Test Outliers"])
df_out = pd.DataFrame(index=ranges, columns=out_columns)

annotated_outliers = state.df_outlier
annotated_test_outliers = state.df_test_outlier
for group_index, (_, group) in enumerate(dataset.groupby("outlier_group")):
outliers_in_this_group = annotated_outliers[
annotated_outliers.index.to_series().between(group.index[0], group.index[-1])
annotated_outliers.index.to_series().between(
group.index[0], group.index[-1]
)
]
test_outliers_in_this_group = annotated_test_outliers[
annotated_test_outliers.index.to_series().between(group.index[0], group.index[-1])
annotated_test_outliers.index.to_series().between(
group.index[0], group.index[-1]
)
]
df_out.iat[group_index, -2] = len(outliers_in_this_group)
df_out.iat[group_index, -1] = len(test_outliers_in_this_group)

for model_index, model in enumerate(model_names):
model_pred_outliers = group[group[model] == 1].index
df_out.iat[group_index, model_index] = len(model_pred_outliers)
df_out.at[ranges[group_index], f"{model} Missed Train Outliers"] = np.count_nonzero(
df_out.at[
ranges[group_index], f"{model} Missed Train Outliers"
] = np.count_nonzero(
outliers_in_this_group.index.isin(model_pred_outliers) == False
)
df_out.at[ranges[group_index], f"{model} Missed Test Outliers"] = np.count_nonzero(
df_out.at[
ranges[group_index], f"{model} Missed Test Outliers"
] = np.count_nonzero(
test_outliers_in_this_group.index.isin(model_pred_outliers) == False
)

Expand Down Expand Up @@ -122,7 +142,9 @@ def make_outlier_distribution_plot(dataset_name: str, series: str):
name="Time Range",
name_location="middle",
name_gap=30,
axistick_opts=opts.AxisTickOpts(is_inside=True, is_align_with_label=True),
axistick_opts=opts.AxisTickOpts(
is_inside=True, is_align_with_label=True
),
),
yaxis_opts=opts.AxisOpts(
type_="value",
Expand Down Expand Up @@ -163,7 +185,9 @@ def make_outlier_distribution_plot(dataset_name: str, series: str):
category_gap="40%",
)

colors = [st.session_state[f"color_{m}_{dataset_name}_{series}"] for m in model_names]
colors = [
st.session_state[f"color_{m}_{dataset_name}_{series}"] for m in model_names
]
if state.outlier:
colors.append("#e60b0b")
if state.test_outlier:
Expand All @@ -179,7 +203,9 @@ def make_outlier_distribution_plot(dataset_name: str, series: str):
if st.session_state.get(f"highlight_train_{dataset_name}_{series}"):
df_missed = df_counts[df_counts[f"{m} Missed Train Outliers"] > 0]
if not df_missed.empty:
effect_scatter = (EffectScatter().add_xaxis(df_missed.index.tolist())).add_yaxis(
effect_scatter = (
EffectScatter().add_xaxis(df_missed.index.tolist())
).add_yaxis(
f"{m} Missed Training Outliers",
df_missed[m].tolist(),
label_opts=opts.LabelOpts(is_show=False),
Expand All @@ -191,7 +217,9 @@ def make_outlier_distribution_plot(dataset_name: str, series: str):
if st.session_state.get(f"highlight_test_{dataset_name}_{series}"):
df_missed = df_counts[df_counts[f"{m} Missed Test Outliers"] > 0]
if not df_missed.empty:
effect_scatter = (EffectScatter().add_xaxis(df_missed.index.tolist())).add_yaxis(
effect_scatter = (
EffectScatter().add_xaxis(df_missed.index.tolist())
).add_yaxis(
f"{m} Missed Test Outliers",
df_missed[m].tolist(),
label_opts=opts.LabelOpts(is_show=False),
Expand Down Expand Up @@ -220,7 +248,10 @@ def _get_start_and_end_date(clicked_range: str):
start_str, end_str = clicked_range.split(" - ")
start_time = datetime.datetime.strptime(start_str, "%Y-%m-%d %H:%M:%S")
end_time = datetime.datetime.strptime(end_str, "%Y-%m-%d %H:%M:%S")
st.session_state[f"last_clicked_range_{dataset_name}_{series}"] = start_time, end_time
st.session_state[f"last_clicked_range_{dataset_name}_{series}"] = (
start_time,
end_time,
)
# st.session_state[f"range_str_{dataset_name}"] = clicked_range
return start_time, end_time

Expand Down Expand Up @@ -265,7 +296,9 @@ def make_annotation_suggestion_plot(
name_location="middle",
name_gap=-20,
),
datazoom_opts=opts.DataZoomOpts(type_="inside", range_start=0, range_end=100),
datazoom_opts=opts.DataZoomOpts(
type_="inside", range_start=0, range_end=100
),
legend_opts=opts.LegendOpts(pos_top=40, pos_right=10, orient="vertical"),
tooltip_opts=opts.TooltipOpts(axis_pointer_type="line", trigger="axis"),
)
Expand All @@ -280,7 +313,7 @@ def make_annotation_suggestion_plot(
label_opts=opts.LabelOpts(is_show=False),
symbol_size=3,
itemstyle_opts=opts.ItemStyleOpts(color="#dce4e3"),
is_selected=len(x_data) < 10000,
# is_selected=len(x_data) < 10000,
tooltip_opts=opts.TooltipOpts(is_show=False),
)
)
Expand Down Expand Up @@ -317,7 +350,9 @@ def get_echarts_plot_time_range(
x_data = state.df_plot.index.to_list()
y_data = state.df_plot[data_column].to_list()
plot = (
Line(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(animation=False)))
Line(
init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(animation=False))
)
.add_xaxis(x_data)
.add_yaxis(
data_column,
Expand Down Expand Up @@ -380,7 +415,7 @@ def get_echarts_plot_time_range(
label_opts=opts.LabelOpts(is_show=False),
symbol_size=3,
itemstyle_opts=opts.ItemStyleOpts(color="#dce4e3"),
is_selected=len(x_data) < 10000,
# is_selected=len(x_data) < 10000,
tooltip_opts=opts.TooltipOpts(is_show=False),
)
)
Expand Down Expand Up @@ -410,7 +445,9 @@ def get_echarts_plot_time_range(
symbol="roundRect",
symbol_size=15,
# color="#dce4e3",
itemstyle_opts=opts.ItemStyleOpts(opacity=1, color=ANNOTATION_COLORS[series_name]),
itemstyle_opts=opts.ItemStyleOpts(
opacity=1, color=ANNOTATION_COLORS[series_name]
),
tooltip_opts=opts.TooltipOpts(is_show=False),
)
)
Expand Down Expand Up @@ -451,13 +488,18 @@ def make_time_range_outlier_plot(dataset_name: str, series: str, start_time, end
symbol="pin",
symbol_size=40,
itemstyle_opts=opts.ItemStyleOpts(
opacity=1, color=st.session_state[f"color_{model_name}_{dataset_name}_{series}"]
opacity=1,
color=st.session_state[
f"color_{model_name}_{dataset_name}_{series}"
],
),
tooltip_opts=opts.TooltipOpts(formatter="{a} <br>Outlier predicted"),
)
)

st.session_state["pred_outlier_tracker"][dataset_name][series] = pred_outlier_tracker
st.session_state["pred_outlier_tracker"][dataset_name][
series
] = pred_outlier_tracker

clicked_point = st_pyecharts(
plot,
Expand Down Expand Up @@ -485,12 +527,18 @@ def feature_importance_plot(base_obj=None):
df_new: pd.DataFrame = st.session_state[f"current_importances_{dataset}_{series}"]

if f"previous_importances_{dataset}_{series}" in st.session_state:
df_old: pd.DataFrame = st.session_state[f"previous_importances_{dataset}_{series}"]
df_plot = df_new.merge(df_old, how="left", on="Feature", suffixes=("", " before"))
df_old: pd.DataFrame = st.session_state[
f"previous_importances_{dataset}_{series}"
]
df_plot = df_new.merge(
df_old, how="left", on="Feature", suffixes=("", " before")
)
df_plot["diff"] = (
df_plot["Feature importance"] - df_plot["Feature importance before"]
).round(3)
df_plot["diff_text"] = df_plot["diff"].apply(lambda x: str(x) if x <= 0 else f"+{x}")
df_plot["diff_text"] = df_plot["diff"].apply(
lambda x: str(x) if x <= 0 else f"+{x}"
)
else:
df_plot = df_new

Expand Down Expand Up @@ -549,15 +597,21 @@ def make_removed_outliers_example_plots(df_before: pd.DataFrame, df_new: pd.Data
number_outlier = outlier_mask.sum()
number_to_show = min(number_outlier, 3)
changes_sample = (
df_before[df_before[f"{s}_{model}"] == 1].sample(number_to_show, random_state=1).index
df_before[df_before[f"{s}_{model}"] == 1]
.sample(number_to_show, random_state=1)
.index
)

st.subheader(s)

cols = st.columns(3)
cols[0].metric("Number of predicted outliers in this series", number_outlier)
cols[1].metric("Number of non-NaN entries before", len(df_before[~df_before[s].isna()]))
cols[2].metric("Number of non-NaN entries after", len(df_new[~df_new[s].isna()]))
cols[1].metric(
"Number of non-NaN entries before", len(df_before[~df_before[s].isna()])
)
cols[2].metric(
"Number of non-NaN entries after", len(df_new[~df_new[s].isna()])
)

if not number_outlier:
continue
Expand All @@ -568,12 +622,12 @@ def make_removed_outliers_example_plots(df_before: pd.DataFrame, df_new: pd.Data
end_idx = min(len(df_before) - 1, int_idx + 20)
start_time = df_before.index[start_idx]
end_time = df_before.index[end_idx]
df_plot_before = df_before[df_before.index.to_series().between(start_time, end_time)][
[s]
].dropna()
df_plot_new = df_new[df_new.index.to_series().between(start_time, end_time)][
[s]
].dropna()
df_plot_before = df_before[
df_before.index.to_series().between(start_time, end_time)
][[s]].dropna()
df_plot_new = df_new[
df_new.index.to_series().between(start_time, end_time)
][[s]].dropna()

plot = (
Line()
Expand Down Expand Up @@ -615,8 +669,12 @@ def make_removed_outliers_example_plots(df_before: pd.DataFrame, df_new: pd.Data
name_location="middle",
name_gap=-20,
),
datazoom_opts=opts.DataZoomOpts(type_="inside", range_start=30, range_end=70),
legend_opts=opts.LegendOpts(pos_top=40, pos_right=10, orient="vertical"),
datazoom_opts=opts.DataZoomOpts(
type_="inside", range_start=30, range_end=70
),
legend_opts=opts.LegendOpts(
pos_top=40, pos_right=10, orient="vertical"
),
tooltip_opts=opts.TooltipOpts(axis_pointer_type="line", trigger="axis"),
)

Expand Down