Visualize sequential data with line plot (#500)

sdmetrics/reports/utils.py

@@ -25,6 +25,8 @@ class PlotConfig:
     DATACEBO_GREEN = '#01E0C9'
     DATACEBO_BLUE = '#03AFF1'
     BACKGROUND_COLOR = '#F5F5F8'
+    DATACEBO_DARK_TRANSPARENT = 'rgba(0, 0, 54, 0.25)'
+    DATACEBO_GREEN_TRANSPARENT = 'rgba(1, 224, 201, 0.25)'
     FONT_SIZE = 18
 
 

sdmetrics/visualization.py

@@ -5,6 +5,7 @@
 import pandas as pd
 import plotly.express as px
 import plotly.figure_factory as ff
+import plotly.graph_objects as go
 import plotly.io as pio
 from pandas.api.types import is_datetime64_dtype
 
@@ -512,3 +513,186 @@ def get_column_pair_plot(real_data, synthetic_data, column_names, plot_type=None
         return _generate_heatmap_plot(all_data, columns)
 
     return _generate_box_plot(all_data, columns)
+
+
+def _generate_line_plot(real_data, synthetic_data, x_axis, y_axis, marker, annotations=None):
+    """Generate a line plot of the real and synthetic data separated by a marker column.
+
+    Args:
+        real_data (pandas.DataFrame):
+            The real table data.
+        synthetic_column (pandas.Dataframe):
+            The synthetic table data.
+        x_axis (str):
+            The column name to be used as the x-axis of the graph
+        y_axis (str):
+            The column name to be used as the y-axis of the graph
+        marker (str):
+            The column used to define separate line sequences
+        annotations (None or dict):
+            Dict object that describes additional information to be presented in the graph
+
+    Returns:
+        plotly.graph_objects._figure.Figure
+    """
+    # Check if the column is the appropriate type
+    all_data = pd.concat([real_data, synthetic_data], axis=0, ignore_index=True)
+    if not (is_datetime(all_data[x_axis]) or
+            pd.api.types.is_numeric_dtype(all_data[x_axis])):
+        raise ValueError(
+            f"Sequence Index '{x_axis}' must contain numerical or datetime values only")
+    if not (is_datetime(all_data[y_axis]) or
+            pd.api.types.is_numeric_dtype(all_data[y_axis])):
+        raise ValueError(
+            f"Column Name '{y_axis}' must contain numerical or datetime values only")
+
+    fig = px.line(all_data, x=x_axis, y=y_axis, color=marker,
+                  color_discrete_map={
+                      'Real': PlotConfig.DATACEBO_DARK,
+                      'Synthetic': PlotConfig.DATACEBO_GREEN
+                  })
+    if annotations:
+        fig.add_annotation(annotations)
+
+    if x_axis == 'sequence_index':
+        fig.update_xaxes(title_text='Sequence Position')
+
+    fig.update_layout(
+        title_text=f"Real vs Synthetic Data for column: '{y_axis}'",
+        plot_bgcolor=PlotConfig.BACKGROUND_COLOR,
+        font={'size': PlotConfig.FONT_SIZE},
+    )
+
+    # Add min-max shading
+    if 'min' in all_data and 'max' in all_data:
+        fig.add_trace(
+            go.Scatter(
+                name='Real-Min',
+                x=real_data[x_axis],
+                y=real_data['min'],
+                hoverinfo='skip',
+                marker={'color': PlotConfig.DATACEBO_DARK_TRANSPARENT},
+                showlegend=False,
+                mode='lines'
+            )
+        )
+        fig.add_trace(
+            go.Scatter(
+                name='Real-Max',
+                x=real_data[x_axis],
+                y=real_data['max'],
+                hoverinfo='skip',
+                marker={'color': PlotConfig.DATACEBO_DARK_TRANSPARENT},
+                showlegend=False,
+                mode='lines',
+                fill='tonexty',
+                fillcolor=PlotConfig.DATACEBO_DARK_TRANSPARENT,
+            )
+        )
+        fig.add_trace(
+            go.Scatter(
+                name='Synthetic-Min',
+                x=synthetic_data[x_axis],
+                y=synthetic_data['min'],
+                hoverinfo='skip',
+                marker={'color': PlotConfig.DATACEBO_GREEN_TRANSPARENT},
+                showlegend=False,
+                mode='lines'
+            )
+        )
+        fig.add_trace(
+            go.Scatter(
+                name='Synthetic-Max',
+                x=synthetic_data[x_axis],
+                y=synthetic_data['max'],
+                hoverinfo='skip',
+                marker={'color': PlotConfig.DATACEBO_GREEN_TRANSPARENT},
+                showlegend=False,
+                mode='lines',
+                fill='tonexty',
+                fillcolor=PlotConfig.DATACEBO_GREEN_TRANSPARENT,
+            )
+        )
+    return fig
+
+
+def get_column_line_plot(real_data, synthetic_data, column_name, metadata):
+    """Return a line plot of the real and synthetic data.
+
+    Args:
+        real_data (pandas.DataFrame):
+            The real table data.
+        synthetic_column (pandas.Dataframe):
+            The synthetic table data.
+        column_name (str):
+            The column name to be used as the y-axis of the graph
+        metadata (dict):
+            TimeSeries metadata dict. If not passed, the graph will
+            use raw indices to build the graph and only separate the sequences
+            into real and synthetic plots
+
+    Returns:
+        plotly.graph_objects._figure.Figure
+    """
+    real_column = real_data[column_name]
+    synthetic_column = synthetic_data[column_name]
+
+    missing_data_real = get_missing_percentage(real_column)
+    missing_data_synthetic = get_missing_percentage(synthetic_column)
+    show_missing_values = missing_data_real > 0 or missing_data_synthetic > 0
+
+    annotations = None if not show_missing_values else {
+        'xref': 'paper',
+        'yref': 'paper',
+        'x': 1.0,
+        'y': 1.05,
+        'showarrow': False,
+        'text': (
+                f'*Missing Values: Real Data ({missing_data_real}%), '
+                f'Synthetic Data ({missing_data_synthetic}%)'
+        ),
+    }
+
+    # Merge the real and synthetic data and add a flag ``Data`` to indicate each one.
+    r_data = real_data.copy()
+    s_data = synthetic_data.copy()
+
+    # Check for sequence index to determine the x-axis values
+    x_axis = 'sequence_index'
+    y_axis = column_name
+    if 'sequence_index' in metadata:
+        x_axis = metadata['sequence_index']
+        if 'sequence_key' in metadata:
+            r_data = r_data.groupby(x_axis, as_index=False).agg(
+                {
+                    x_axis: 'first',
+                    column_name: ['mean', 'min', 'max']
+                }
+            ).rename(columns={'mean': column_name, 'first': x_axis})
+            s_data = s_data.groupby(x_axis, as_index=False).agg(
+                {
+                    x_axis: 'first',
+                    column_name: ['mean', 'min', 'max']
+                }
+            ).rename(columns={'mean': column_name, 'first': x_axis})
+
+            r_data.columns = r_data.columns.droplevel(0)
+            s_data.columns = s_data.columns.droplevel(0)
+    else:
+        r_data['sequence_index'] = r_data.index
+        s_data['sequence_index'] = s_data.index
+
+    marker_name = 'Data'
+    r_data[marker_name] = 'Real'
+    s_data[marker_name] = 'Synthetic'
+
+    # Generate plot
+    fig = _generate_line_plot(
+        real_data=r_data,
+        synthetic_data=s_data,
+        x_axis=x_axis,
+        y_axis=y_axis,
+        marker=marker_name,
+        annotations=annotations
+    )
+    return fig