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main.py

+import os
+import click
+import pandas as pd
+
+# pylint: disable=no-value-for-parameter
+
+
+def initial_idea(x, y, w1, w2):
+    return max(1, min(5, x + (y - 0.5) * w1) - (1 - y) / w2 * x)
+
+
+def compute_contrip(x, y, w1, w2):
+    return min(5, x + (y - 0.5) * w1) - (1 - y) / w2 * x - (5 - x) / 100
+
+
+def compute_contrip_now2(x, y, w1):
+    return min(5, x + (y - 0.5) * w1) - (1 - y) / (6 - x)
+
+
+def scaling(score):
+    return (score - 0.61) / (5 - 0.61) * 4 + 1
+
+
+# ------------------------------------------------------------------------------
+# CLICK
+# ------------------------------------------------------------------------------
+
+
+@click.command(short_help="script to compute the score of a tripadvisor rating")
+@click.option(
+    "-tr", "--tripadvisor_rating", default=5.0, help="rating of tripadvisor hotel"
+)
+@click.option(
+    "-cv",
+    "--consensus_value",
+    default=1.0,
+    help="value of consensus analysis through NLP",
+)
+@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("-o", "--output", default="", help="output folder")
+def main(tripadvisor_rating, consensus_value, weight_1, weight_2, output):
+
+    assert tripadvisor_rating >= 1
+
+    score = compute_contrip(tripadvisor_rating, consensus_value, weight_1, weight_2)
+    print("ConTrip Score: {}".format(score))
+
+    new_score = scaling(score)
+    print("ConTrip Score: {}".format(new_score))
+
+    df = pd.DataFrame([[score, new_score]], columns=["ConTrip", "ConTrip Scaled"])
+
+    out_file = os.path.join(output, "output_score.tsv")
+    df.to_csv(out_file, index=None, sep="\t")
+
+
+if __name__ == "__main__":
+    main()