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comparison_figures.py

+import os
+import click
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# pylint: disable=no-value-for-parameter
+
+
+def compute_contrip(x, y, w1, w2):
+
+    return (
+        np.minimum(np.full((y.shape[0], x.shape[1]), 5), x + (y - 0.5) * w1)
+        - (1 - y) / w2 * x
+        - (5 - x) / 100
+    )
+
+
+def scaling(score):
+    return (score - 0.61) / (5 - 0.61) * 4 + 1
+
+
+def generate_dataframe(score, consensus):
+
+    df = pd.DataFrame()
+    df["ConTrip Score"] = np.ndarray.flatten(score, "F")
+    df["Consensus Score"] = np.asarray([consensus] * 5).flatten()
+    df["Rating"] = np.asarray(
+        [
+            ["Rating: 1"] * 51,
+            ["Rating: 2"] * 51,
+            ["Rating: 3"] * 51,
+            ["Rating: 4"] * 51,
+            ["Rating: 5"] * 51,
+        ]
+    ).flatten()
+
+    return df
+
+
+def generate_dataframe_2(score, rating):
+
+    df = pd.DataFrame()
+    df["ConTrip Score"] = score.flatten()
+    df["Rating"] = np.asarray([rating] * 6).flatten()
+    df["Consensus Score"] = np.asarray(
+        [
+            ["Consensus: 0.0"] * 41,
+            ["Consensus: 0.2"] * 41,
+            ["Consensus: 0.4"] * 41,
+            ["Consensus: 0.6"] * 41,
+            ["Consensus: 0.8"] * 41,
+            ["Consensus: 1.0"] * 41,
+        ]
+    ).flatten()
+
+    return df
+
+
+def generate_plots_1(df, output, scale):
+
+    fig, ax = plt.subplots(figsize=(5, 5))
+
+    sns.scatterplot(
+        data=df,
+        x="Consensus Score",
+        y="ConTrip Score",
+        hue="Rating",
+        palette=["#d7191c", "#fdae61", "#fee08b", "#abdda4", "#2b83ba"],
+    )
+
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+
+    custom_lines = []
+    for el in [
+        ["Rating: 1", "#d7191c"],
+        ["Rating: 2", "#fdae61"],
+        ["Rating: 3", "#fee08b"],
+        ["Rating: 4", "#abdda4"],
+        ["Rating: 5", "#2b83ba"],
+    ]:
+
+        custom_lines.append(
+            plt.plot(
+                [],
+                [],
+                marker="o",
+                ms=7,
+                ls="",
+                mec="black",
+                mew=0,
+                color=el[1],
+                label=el[0],
+            )[0]
+        )
+
+    ax.legend(
+        bbox_to_anchor=(0.0, 1.05, 1.0, 0.102),
+        handles=custom_lines,
+        loc="upper center",
+        facecolor="white",
+        ncol=3,
+        fontsize=8,
+        frameon=False,
+    )
+
+    fig.tight_layout()
+
+    if scale:
+        out_file = os.path.join(output, "contrip_score_consensus_scaling.pdf")
+    else:
+        out_file = os.path.join(output, "contrip_score_consensus.pdf")
+
+    plt.savefig(out_file)
+    plt.close()
+
+
+def generate_plots_2(df, output, scale):
+
+    fig, ax = plt.subplots(figsize=(5, 5))
+
+    sns.scatterplot(
+        data=df,
+        x="Rating",
+        y="ConTrip Score",
+        hue="Consensus Score",
+        palette=["#d7191c", "#fdae61", "#fee08b", "#abdda4", "#2b83ba", "#5e4fa2"],
+    )
+
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+
+    custom_lines = []
+    for el in [
+        ["Consensus: 0.0", "#d7191c"],
+        ["Consensus: 0.2", "#fdae61"],
+        ["Consensus: 0.4", "#fee08b"],
+        ["Consensus: 0.6", "#abdda4"],
+        ["Consensus: 0.8", "#2b83ba"],
+        ["Consensus: 1.0", "#5e4fa2"],
+    ]:
+
+        custom_lines.append(
+            plt.plot(
+                [],
+                [],
+                marker="o",
+                ms=7,
+                ls="",
+                mec="black",
+                mew=0,
+                color=el[1],
+                label=el[0],
+            )[0]
+        )
+
+    ax.legend(
+        bbox_to_anchor=(0.0, 1.05, 1.0, 0.102),
+        handles=custom_lines,
+        loc="upper center",
+        facecolor="white",
+        ncol=3,
+        fontsize=8,
+        frameon=False,
+    )
+
+    fig.tight_layout()
+
+    if scale:
+        out_file = os.path.join(output, "contrip_score_rating_scaling.pdf")
+    else:
+        out_file = os.path.join(output, "contrip_score_rating.pdf")
+
+    plt.savefig(out_file)
+    plt.close()
+
+
+def generate_figure_1(weight_1, weight_2, scale, output):
+
+    rating = np.linspace(1, 5, 5).reshape(1, 5)
+    consensus = np.linspace(0, 1, 51).reshape(51, 1)
+
+    score = compute_contrip(rating, consensus, weight_1, weight_2)
+
+    if scale:
+        score = scaling(score)
+
+    df = generate_dataframe(score, consensus)
+
+    generate_plots_1(df, output, scale)
+
+
+def generate_figure_2(weight_1, weight_2, scale, output):
+
+    rating = np.linspace(1, 5, 41).reshape(1, 41)
+    consensus = np.linspace(0, 1, 6).reshape(6, 1)
+
+    score = compute_contrip(rating, consensus, weight_1, weight_2)
+
+    if scale:
+        score = scaling(score)
+
+    df = generate_dataframe_2(score, rating)
+
+    generate_plots_2(df, output, scale)
+
+
+# ------------------------------------------------------------------------------
+# CLICK
+# ------------------------------------------------------------------------------
+
+
+@click.command(
+    short_help="script to study how different parameters change"
+    "the behavior of the score of a tripadvisor rating"
+)
+@click.option(
+    "-w1",
+    "--weight_1",
+    default=0.5,
+    help="weight for the importance of the consensus value",
+)
+@click.option(
+    "-w2",
+    "--weight_2",
+    default=10.0,
+    help="weight for the importance of the consensus value 2",
+)
+@click.option("-sf", "--scaling_flag", is_flag=True, help="flag to perform the scaling")
+@click.option("-o", "--output", default="", help="output folder")
+def main(weight_1, weight_2, scaling_flag, output):
+
+    generate_figure_1(weight_1, weight_2, scaling_flag, output)
+    generate_figure_2(weight_1, weight_2, scaling_flag, output)
+
+
+if __name__ == "__main__":
+    main()