)
LENGTH_ENCODE = True
if LENGTH_ENCODE:
len_encode = ["URL"]
for col in len_encode:
X[f"{col}_len"] = X[col].apply(len)
X = X.drop(col, axis=1)
CATEGORIZE = False
if CATEGORIZE:
X[obj_cols] = X[obj_cols].astype("category")
DATE_ENCODE = False
if DATE_ENCODE:
X = encode_dates(X, "date")
sns.displot(y)
plt.title("Distribution")
plt.show()
SEED = 0
SAMPLE_SIZE = 10000
Xt, Xv, yt, yv = train_test_split(
X, y, random_state=SEED) # split into train and validation set
dt = lgb.Dataset(Xt, yt, free_raw_data=False)
np.random.seed(SEED)
sample_idx = np.random.choice(Xt.index, size=SAMPLE_SIZE)
Xs, ys = Xt.loc[sample_idx], yt.loc[sample_idx]
ds = lgb.Dataset(Xs, ys)
X = df.drop(
[
"Termd",
"EmploymentStatus",
"DateofTermination",
"LastPerformanceReview_Date",
"EmpStatusID",
"TermReason",
],
axis=1,
)
X.info()
date_cols = X.select_dtypes("datetime")
for col in date_cols:
X = encode_dates(X, col)
encode_columns = ["Employee_Name", "Position", "ManagerName"]
enc = SimilarityEncoder(similarity="ngram", categories="k-means", n_prototypes=4)
for col in encode_columns:
transformed_values = enc.fit_transform(X[col].values.reshape(-1, 1))
transformed_values = pd.DataFrame(transformed_values, index=X.index)
transformed_values.columns = [f"{col}_" + str(num) for num in transformed_values]
X = pd.concat([X, transformed_values], axis=1)
X = X.drop(col, axis=1)
obj_cols = X.select_dtypes("object").columns
X[obj_cols] = X[obj_cols].astype("category")
SEED = 0
count += 1
return count
def get_female_count(x):
count = 0
for gender in x.split(", "):
if gender == "female":
count += 1
return count
X["male_count"] = X["borrower_genders"].apply(get_male_count)
X["female_count"] = X["borrower_genders"].apply(get_female_count)
X = encode_dates(X, "posted_time")
X = encode_dates(X, "disbursed_time")
X = encode_dates(X, "funded_time")
X = encode_dates(X, "date")
SEED = 0
SAMPLE_SIZE = 5000
Xt, Xv, yt, yv = train_test_split(
X, y, random_state=SEED
) # split into train and validation set
dt = lgb.Dataset(Xt, yt, free_raw_data=False)
np.random.seed(SEED)
sample_idx = np.random.choice(Xt.index, size=SAMPLE_SIZE)
Xs, ys = Xt.loc[sample_idx], yt.loc[sample_idx]
ds = lgb.Dataset(Xs, ys)
LENGTH_ENCODE = False
if LENGTH_ENCODE:
len_encode = ["URL"]
for col in len_encode:
X[f"{col}_len"] = X[col].apply(len)
X = X.drop(col, axis=1)
CATEGORIZE = True
if CATEGORIZE:
X[obj_cols] = X[obj_cols].astype("category")
enc = OrdinalEncoder()
X = enc.fit_transform(X)
DATE_ENCODE = False
if DATE_ENCODE:
X = encode_dates(X, "date")
sns.displot(y)
plt.title("Distribution")
plt.show()
SEED = 0
SAMPLE_SIZE = 10000
Xt, Xv, yt, yv = train_test_split(
X, y, random_state=SEED) # split into train and validation set
dt = xgb.DMatrix(Xt, yt)
np.random.seed(SEED)
sample_idx = np.random.choice(Xt.index, size=SAMPLE_SIZE)
Xs, ys = Xt.loc[sample_idx], yt.loc[sample_idx]
ds = xgb.DMatrix(Xs, ys)
for weightclass in unique_plus_classes:
try:
weightclass = float(weightclass[:-1])
except ValueError:
weightclass = 0
float_unique_plus_classes.append(weightclass)
X["WeightClassKg"] = (
X["WeightClassKg"]
.replace(
unique_plus_classes,
float_unique_plus_classes,
)
.astype(float)
)
X = encode_dates(X, 'Date')
obj_cols = X.select_dtypes("object").columns
nunique = X[obj_cols].nunique()
prop_unique = (X[obj_cols].nunique() / len(df)).sort_values(
ascending=False
) # in order of most unique to least
unique = pd.concat([prop_unique, nunique], axis=1)
unique.columns = [
"proportion",
"nunique",
]
unique
X = similarity_encode(
X,
