--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/plot_regression_performance.py	Thu Nov 08 13:28:04 2018 -0500
@@ -0,0 +1,98 @@
+import argparse
+import pandas as pd
+import plotly
+import plotly.graph_objs as go
+
+
+def main(infile_input, infile_output):
+    """
+    Produce an interactive actual vs predicted curves and residual plots
+    Args:
+        infile_input: str, input tabular file with true values
+        infile_output: str, input tabular file with predicted values
+    """
+
+    df_input = pd.read_csv(infile_input, sep='\t', parse_dates=True)
+    df_output = pd.read_csv(infile_output, sep='\t', parse_dates=True)
+    true_values = df_input.iloc[:, -1].copy()
+    predicted_values = df_output.iloc[:, -1].copy()
+    axis_labels = list(range(1, len(true_values)+1))
+
+    # true vs predicted curves
+    trace_true = go.Scatter(
+        x=axis_labels,
+        y=true_values,
+        mode='lines+markers',
+        name='True values'
+    )
+
+    trace_predicted = go.Scatter(
+        x=axis_labels,
+        y=predicted_values,
+        mode='lines+markers',
+        name='Predicted values'
+    )
+
+    layout_tp = go.Layout(
+        title='True vs predicted values',
+        xaxis=dict(title='Number of data points'),
+        yaxis=dict(title='Values')
+    )
+
+    data_tp = [trace_true, trace_predicted]
+    fig_tp = go.Figure(data=data_tp, layout=layout_tp)
+    plotly.offline.plot(fig_tp, filename="output_actual_vs_pred.html", auto_open=False)
+
+    # scatter plot
+    max_tv = int(max(true_values))
+    x_y_values = list(range(0, max_tv))
+
+    trace_x_eq_y = go.Scatter(
+        x=x_y_values,
+        y=x_y_values,
+        mode='lines',
+        name='X = Y curve'
+    )
+
+    trace_true_pred = go.Scatter(
+        x=true_values,
+        y=predicted_values,
+        mode='markers',
+        name='True and predicted values'
+    )
+
+    layout_true_pred = go.Layout(
+        title='True vs predicted values',
+        xaxis=dict(title='True values'),
+        yaxis=dict(title='Predicted values')
+    )
+
+    data_true_pred = [trace_true_pred, trace_x_eq_y]
+    fig_true_pred = go.Figure(data=data_true_pred, layout=layout_true_pred)
+    plotly.offline.plot(fig_true_pred, filename="output_scatter_plot.html", auto_open=False)
+
+    # residual plot
+    residual = predicted_values - true_values
+    trace_residual = go.Scatter(
+        x=predicted_values,
+        y=residual,
+        mode='markers'
+    )
+
+    layout_residual = go.Layout(
+        title='Residual vs predicted values',
+        xaxis=dict(title='Predicted values'),
+        yaxis=dict(title='Residual (Predicted - True)')
+    )
+
+    data_residual = [trace_residual]
+    fig_residual = go.Figure(data=data_residual, layout=layout_residual)
+    plotly.offline.plot(fig_residual, filename="output_residual_plot.html", auto_open=False)
+
+
+if __name__ == "__main__":
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--input", dest="infile_input", required=True)
+    aparser.add_argument("-j", "--output", dest="infile_output", required=True)
+    args = aparser.parse_args()
+    main(args.infile_input, args.infile_output)