[Feature-selection] Replace matplotlib with plotly (#815)

feature_selection/feature_selection.py

@@ -13,17 +13,15 @@
 # limitations under the License.
 #
 import json
-import os
 
-import matplotlib.pyplot as plt
 import mlrun
 import mlrun.datastore
-import mlrun.utils
 import mlrun.feature_store as fs
+import mlrun.utils
 import numpy as np
 import pandas as pd
-import seaborn as sns
-from mlrun.artifacts import PlotArtifact
+import plotly.express as px
+from mlrun.artifacts import PlotlyArtifact
 from mlrun.datastore.targets import ParquetTarget
 # MLRun utils
 from mlrun.utils.helpers import create_class
@@ -42,15 +40,6 @@
 }
 
 
-def _clear_current_figure():
-    """
-    Clear matplotlib current figure.
-    """
-    plt.cla()
-    plt.clf()
-    plt.close()
-
-
 def show_values_on_bars(axs, h_v="v", space=0.4):
     def _show_on_single_plot(ax_):
         if h_v == "v":
@@ -74,33 +63,18 @@ def _show_on_single_plot(ax_):
 
 
 def plot_stat(context, stat_name, stat_df):
-    _clear_current_figure()
-
-    # Add chart
-    ax = plt.axes()
-    stat_chart = sns.barplot(
+    sorted_df = stat_df.sort_values(stat_name)
+    fig = px.bar(
+        data_frame=sorted_df,
         x=stat_name,
-        y="index",
-        data=stat_df.sort_values(stat_name, ascending=False).reset_index(),
-        ax=ax,
+        y=sorted_df.index,
+        title=f"{stat_name} feature scores",
+        color=stat_name,
     )
-    plt.tight_layout()
-
-    for p in stat_chart.patches:
-        width = p.get_width()
-        plt.text(
-            5 + p.get_width(),
-            p.get_y() + 0.55 * p.get_height(),
-            "{:1.2f}".format(width),
-            ha="center",
-            va="center",
-        )
-
     context.log_artifact(
-        PlotArtifact(f"{stat_name}", body=plt.gcf()),
-        local_path=os.path.join("plots", "feature_selection", f"{stat_name}.html"),
+        item=PlotlyArtifact(key=stat_name, figure=fig),
+        local_path=f"{stat_name}.html",
     )
-    _clear_current_figure()
 
 
 def feature_selection(
@@ -115,7 +89,6 @@ def feature_selection(
     sample_ratio: float = None,
     output_vector_name: float = None,
     ignore_type_errors: bool = False,
-    is_feature_vector: bool = False,
 ):
     """
     Applies selected feature selection statistical functions or models on our 'df_artifact'.
@@ -138,10 +111,9 @@ def feature_selection(
                                 model name (ex. LinearSVC), formalized json (contains 'CLASS',
                                 'FIT', 'META') or a path to such json file.
     :param max_scaled_scores:   produce feature scores table scaled with max_scaler.
-    :param sample_ratio:        percentage of the dataset the user whishes to compute the feature selection process on.
+    :param sample_ratio:        percentage of the dataset the user wishes to compute the feature selection process on.
     :param output_vector_name:  creates a new feature vector containing only the identifies features.
     :param ignore_type_errors:  skips datatypes that are neither float nor int within the feature vector.
-    :param is_feature_vector:   bool stating if the data is passed as a feature vector.
     """
     stat_filters = stat_filters or DEFAULT_STAT_FILTERS
     model_filters = model_filters or DEFAULT_MODEL_FILTERS