adding categorical caching

.gitignore

@@ -1 +1,4 @@
 .venv
+out
+.DS_Store
+__pycache__

busi410project/categorize.py

@@ -0,0 +1,54 @@
+import numpy as np
+import pandas as pd
+
+
+def categorize():
+    df = pd.read_csv("./data/imdb_top_2000_movies.csv", index_col=0)
+    print(f"initial number of movies: {len(df.index)}")
+
+    df = df[df[["Metascore"]].notnull().all(1)]
+
+    df["Gross"] = df["Gross"].str.replace(r"\D", "", regex=True).astype(float) * 1e6
+    df["Release Year"] = df["Release Year"].str.replace(r"\D", "", regex=True).str[:4]
+
+    df["Duration"] = df["Duration"].astype(int)
+    df["Votes"] = df["Votes"].str.replace(r"\D", "", regex=True).astype(int)
+
+    df["IMDB Rating"] = df["IMDB Rating"].astype(float)
+    df["Metascore"] = df["Metascore"].astype(int)
+
+    df["Director"] = pd.Categorical(df["Director"])
+    df["Cast"] = pd.Categorical(df["Cast"])
+    df["Release Year"] = pd.Categorical(df["Release Year"])
+
+    director_cat = np.array(df["Director"].cat.categories)
+    actor_cat = np.array(df["Cast"].cat.categories)
+    release_year_cat = np.array(df["Release Year"].cat.categories)
+
+    df[["Genre 1", "Genre 2", "Genre 3"]] = (
+        df["Genre"].str.split(", ", expand=True).astype("category")
+    )
+
+    genre_cat = np.unique(
+        np.concatenate(
+            [
+                df["Genre 1"].cat.categories,
+                df["Genre 2"].cat.categories,
+                df["Genre 3"].cat.categories,
+            ]
+        )
+    )
+
+    df.drop(columns=["Genre"], inplace=True)
+
+    df = df[
+        df[["Gross", "IMDB Rating", "Metascore", "Release Year", "Votes"]]
+        .notnull()
+        .all(1)
+    ]
+    print(f"number of movies after cleaning: {len(df.index)}")
+
+    with open("./out/categoricals.csv", "w") as f:
+        f.write(df.to_csv())
+
+    return df

busi410project/fit.py

@@ -1,54 +1,19 @@
+import os, csv
 import numpy as np
 import pandas as pd
 import statsmodels.api as sm
 import statsmodels.formula.api as smf
 from patsy import dmatrices
+from categorize import categorize
 
 
-df = pd.read_csv("./data/imdb_top_2000_movies.csv", index_col=0)
-print(f"initial number of movies: {len(df.index)}")
+if not os.path.exists("./out"):
+    os.makedirs("./out")
 
-
-df = df[df[["Metascore"]].notnull().all(1)]
-
-df["Gross"] = df["Gross"].str.replace(r"\D", "", regex=True).astype(float) * 1e6
-df["Release Year"] = df["Release Year"].str.replace(r"\D", "", regex=True).str[:4]
-
-df["Duration"] = df["Duration"].astype(int)
-df["Votes"] = df["Votes"].str.replace(r"\D", "", regex=True).astype(int)
-
-df["IMDB Rating"] = df["IMDB Rating"].astype(float)
-df["Metascore"] = df["Metascore"].astype(int)
-
-
-df["Director"] = pd.Categorical(df["Director"])
-df["Cast"] = pd.Categorical(df["Cast"])
-df["Release Year"] = pd.Categorical(df["Release Year"])
-
-director_cat = np.array(df["Director"].cat.categories)
-actor_cat = np.array(df["Cast"].cat.categories)
-release_year_cat = np.array(df["Release Year"].cat.categories)
-
-df[["Genre 1", "Genre 2", "Genre 3"]] = (
-    df["Genre"].str.split(", ", expand=True).astype("category")
-)
-
-genre_cat = np.unique(
-    np.concatenate(
-        [
-            df["Genre 1"].cat.categories,
-            df["Genre 2"].cat.categories,
-            df["Genre 3"].cat.categories,
-        ]
-    )
-)
-
-df.drop(columns=["Genre"], inplace=True)
-
-df = df[
-    df[["Gross", "IMDB Rating", "Metascore", "Release Year", "Votes"]].notnull().all(1)
-]
-print(f"number of movies after cleaning: {len(df.index)}")
+if not os.path.exists("./out/categoricals.csv"):
+    df = categorize()
+else:
+    df = pd.read_csv("./out/categoricals.csv", index_col=0)
 
 
 target = "Gross"
@@ -75,3 +40,6 @@ model = sm.OLS(y, X)
 results = model.fit()
 
 print(results.summary())
+
+with open("./out/model_summary.csv", "w") as f:
+    f.write(results.summary().as_csv())