def test_transform_string_indexer_list(self):
df = pd.DataFrame({"cat_column": ["a", "b", "b"]})
trainable = Map(columns=[string_indexer(it.cat_column)])
trained = trainable.fit(df)
transformed_df = trained.transform(df)
self.assertEqual(transformed_df["cat_column"][0], 1)
self.assertEqual(transformed_df["cat_column"][1], 0)
self.assertEqual(transformed_df["cat_column"][2], 0)
def test_transform_dow_list(self):
df = pd.DataFrame(
{"date_column": ["2016-05-28", "2016-06-28", "2016-07-28"]})
trainable = Map(columns=[day_of_week(it.date_column)])
trained = trainable.fit(df)
transformed_df = trained.transform(df)
self.assertEqual(transformed_df["date_column"][0], 5)
self.assertEqual(transformed_df["date_column"][1], 1)
self.assertEqual(transformed_df["date_column"][2], 3)
def test_transform_string_indexer_list(self):
df = pd.DataFrame({"cat_column": ["a", "b", "b"]})
sdf = self.sqlCtx.createDataFrame(df)
trainable = Map(columns=[string_indexer(it.cat_column)])
trained = trainable.fit(sdf)
transformed_df = trained.transform(sdf)
self.assertEqual(transformed_df.collect()[0]["cat_column"], 1)
self.assertEqual(transformed_df.collect()[1]["cat_column"], 0)
self.assertEqual(transformed_df.collect()[2]["cat_column"], 0)
def test_transform_doy_fmt_list(self):
df = pd.DataFrame(
{"date_column": ["2016-01-01", "2016-06-28", "2016-07-28"]})
trainable = Map(columns=[day_of_year(it.date_column, "%Y-%m-%d")])
trained = trainable.fit(df)
transformed_df = trained.transform(df)
self.assertEqual(transformed_df["date_column"][0], 1)
self.assertEqual(transformed_df["date_column"][1], 180)
self.assertEqual(transformed_df["date_column"][2], 210)
def test_transform_dom_fmt_list(self):
df = pd.DataFrame(
{"date_column": ["2016-05-28", "2016-06-27", "2016-07-26"]})
trainable = Map(columns=[day_of_month(it.date_column, "%Y-%m-%d")])
trained = trainable.fit(df)
transformed_df = trained.transform(df)
self.assertEqual(transformed_df["date_column"][0], 28)
self.assertEqual(transformed_df["date_column"][1], 27)
self.assertEqual(transformed_df["date_column"][2], 26)
def test_transform_string_indexer_map(self):
df = pd.DataFrame({"cat_column": ["a", "b", "b"]})
trainable = Map(
columns={"string_indexed": string_indexer(it.cat_column)})
trained = trainable.fit(df)
transformed_df = trained.transform(df)
self.assertEqual(transformed_df.shape, (3, 1))
self.assertEqual(transformed_df["string_indexed"][0], 1)
self.assertEqual(transformed_df["string_indexed"][1], 0)
self.assertEqual(transformed_df["string_indexed"][2], 0)
def test_transform_dow_fmt_map(self):
df = pd.DataFrame(
{"date_column": ["2016-05-28", "2016-06-28", "2016-07-28"]})
trainable = Map(
columns={"dow": day_of_week(it.date_column, "%Y-%m-%d")})
trained = trainable.fit(df)
transformed_df = trained.transform(df)
self.assertEqual(transformed_df.shape, (3, 1))
self.assertEqual(transformed_df["dow"][0], 5)
self.assertEqual(transformed_df["dow"][1], 1)
self.assertEqual(transformed_df["dow"][2], 3)
def test_transform_dom_list(self):
df = pd.DataFrame(
{"date_column": ["2016-05-28", "2016-06-27", "2016-07-26"]})
sdf = self.sqlCtx.createDataFrame(df)
trainable = Map(columns=[day_of_month(it.date_column)])
trained = trainable.fit(sdf)
transformed_df = trained.transform(sdf)
self.assertEqual(transformed_df.collect()[0]["date_column"], 28)
self.assertEqual(transformed_df.collect()[1]["date_column"], 27)
self.assertEqual(transformed_df.collect()[2]["date_column"], 26)
def test_transform_dom_fmt_list(self):
df = pd.DataFrame(
{"date_column": ["28/05/2016", "27/06/2016", "26/07/2016"]})
sdf = self.sqlCtx.createDataFrame(df)
trainable = Map(columns=[day_of_month(it.date_column, "d/M/y")])
trained = trainable.fit(sdf)
transformed_df = trained.transform(sdf)
self.assertEqual(transformed_df.collect()[0]["date_column"], 28)
self.assertEqual(transformed_df.collect()[1]["date_column"], 27)
self.assertEqual(transformed_df.collect()[2]["date_column"], 26)
def test_transform_doy_fmt_list(self):
df = pd.DataFrame(
{"date_column": ["2016-01-01", "2016-06-28", "2016-07-28"]})
sdf = self.sqlCtx.createDataFrame(df)
trainable = Map(columns=[day_of_year(it.date_column, "y-M-d")])
trained = trainable.fit(sdf)
transformed_df = trained.transform(sdf)
self.assertEqual(transformed_df.collect()[0]["date_column"], 1)
self.assertEqual(transformed_df.collect()[1]["date_column"], 180)
self.assertEqual(transformed_df.collect()[2]["date_column"], 210)
def test_transform_string_indexer_map(self):
df = pd.DataFrame({"cat_column": ["a", "b", "b"]})
sdf = self.sqlCtx.createDataFrame(df)
trainable = Map(
columns={"string_indexed": string_indexer(it.cat_column)})
trained = trainable.fit(sdf)
transformed_df = trained.transform(sdf)
self.assertEqual((transformed_df.count(), len(transformed_df.columns)),
(3, 1))
self.assertEqual(transformed_df.collect()[0]["string_indexed"], 1)
self.assertEqual(transformed_df.collect()[1]["string_indexed"], 0)
self.assertEqual(transformed_df.collect()[2]["string_indexed"], 0)
def test_transform_dow_list(self):
df = pd.DataFrame(
{"date_column": ["2016-05-28", "2016-06-28", "2016-07-28"]})
sdf = self.sqlCtx.createDataFrame(df)
trainable = Map(columns=[day_of_week(it.date_column)])
trained = trainable.fit(sdf)
transformed_df = trained.transform(sdf)
# Note that spark dayofweek outputs are different from pandas
self.assertEqual(transformed_df.collect()[0]["date_column"], 7)
self.assertEqual(transformed_df.collect()[1]["date_column"], 3)
self.assertEqual(transformed_df.collect()[2]["date_column"], 5)
def test_transform_dow_fmt_map(self):
df = pd.DataFrame(
{"date_column": ["2016-05-28", "2016-06-28", "2016-07-28"]})
sdf = self.sqlCtx.createDataFrame(df)
trainable = Map(columns={"dow": day_of_week(it.date_column, "y-M-d")})
trained = trainable.fit(sdf)
transformed_df = trained.transform(sdf)
self.assertEqual((transformed_df.count(), len(transformed_df.columns)),
(3, 1))
self.assertEqual(transformed_df.collect()[0]["dow"], 7)
self.assertEqual(transformed_df.collect()[1]["dow"], 3)
self.assertEqual(transformed_df.collect()[2]["dow"], 5)
def test_transform_minute_list(self):
df = pd.DataFrame({
"date_column": [
"2016-01-01 15:16:45",
"2016-06-28 12:18:51",
"2016-07-28 01:01:01",
]
})
trainable = Map(columns=[minute(it.date_column)])
trained = trainable.fit(df)
transformed_df = trained.transform(df)
self.assertEqual(transformed_df["date_column"][0], 16)
self.assertEqual(transformed_df["date_column"][1], 18)
self.assertEqual(transformed_df["date_column"][2], 1)
def test_transform_month_fmt_list(self):
df = pd.DataFrame({
"date_column": [
"2016-01-01 15:16:45",
"2016-06-28 12:18:51",
"2016-07-28 01:01:01",
]
})
trainable = Map(columns=[month(it.date_column, "%Y-%m-%d %H:%M:%S")])
trained = trainable.fit(df)
transformed_df = trained.transform(df)
self.assertEqual(transformed_df["date_column"][0], 1)
self.assertEqual(transformed_df["date_column"][1], 6)
self.assertEqual(transformed_df["date_column"][2], 7)
def test_transform_minute_fmt_list(self):
df = pd.DataFrame({
"date_column": [
"2016-01-01 15:16:45",
"2016-06-28 12:18:51",
"2016-07-28 01:01:01",
]
})
sdf = self.sqlCtx.createDataFrame(df)
trainable = Map(columns=[minute(it.date_column, "y-M-d HH:mm:ss")])
trained = trainable.fit(sdf)
transformed_df = trained.transform(sdf)
self.assertEqual(transformed_df.collect()[0]["date_column"], 16)
self.assertEqual(transformed_df.collect()[1]["date_column"], 18)
self.assertEqual(transformed_df.collect()[2]["date_column"], 1)
def test_transform_hour_list(self):
df = pd.DataFrame({
"date_column": [
"2016-01-01 15:16:45",
"2016-06-28 12:18:51",
"2016-07-28 01:01:01",
]
})
sdf = self.sqlCtx.createDataFrame(df)
trainable = Map(columns=[hour(it.date_column)])
trained = trainable.fit(sdf)
transformed_df = trained.transform(sdf)
self.assertEqual(transformed_df.collect()[0]["date_column"], 15)
self.assertEqual(transformed_df.collect()[1]["date_column"], 12)
self.assertEqual(transformed_df.collect()[2]["date_column"], 1)
def test_transform_minute_fmt_map(self):
df = pd.DataFrame({
"date_column": [
"2016-01-01 15:16:45",
"2016-06-28 12:18:51",
"2016-07-28 01:01:01",
]
})
trainable = Map(
columns={"minute": minute(it.date_column, "%Y-%m-%d %H:%M:%S")})
trained = trainable.fit(df)
transformed_df = trained.transform(df)
self.assertEqual(transformed_df.shape, (3, 1))
self.assertEqual(transformed_df["minute"][0], 16)
self.assertEqual(transformed_df["minute"][1], 18)
self.assertEqual(transformed_df["minute"][2], 1)
def test_transform_replace_list(self):
d = {
"gender": ["m", "f", "m", "m", "f"],
"state": ["NY", "NY", "CA", "NY", "CA"],
"status": [0, 1, 1, 0, 1],
}
df = pd.DataFrame(data=d)
gender_map = {"m": "Male", "f": "Female"}
state_map = {"NY": "New York", "CA": "California"}
trainable = Map(columns=[
replace(it.gender, gender_map),
replace(it.state, state_map)
])
trained = trainable.fit(df)
transformed_df = trained.transform(df)
self.assertEqual(transformed_df.shape, (5, 3))
self.assertEqual(transformed_df["gender"][0], "Male")
self.assertEqual(transformed_df["state"][0], "New York")
def test_transform_month_fmt_map(self):
df = pd.DataFrame({
"date_column": [
"2016-01-01 15:16:45",
"2016-06-28 12:18:51",
"2016-07-28 01:01:01",
]
})
sdf = self.sqlCtx.createDataFrame(df)
trainable = Map(
columns={"month": month(it.date_column, "y-M-d HH:mm:ss")})
trained = trainable.fit(sdf)
transformed_df = trained.transform(sdf)
self.assertEqual((transformed_df.count(), len(transformed_df.columns)),
(3, 1))
self.assertEqual(transformed_df.collect()[0]["month"], 1)
self.assertEqual(transformed_df.collect()[1]["month"], 6)
self.assertEqual(transformed_df.collect()[2]["month"], 7)
def test_transform_spark_replace_list(self):
if spark_installed:
d = {
"gender": ["m", "f", "m", "m", "f"],
"state": ["NY", "NY", "CA", "NY", "CA"],
"status": [0, 1, 1, 0, 1],
}
df = pd.DataFrame(data=d)
sdf = self.sqlCtx.createDataFrame(df)
gender_map = {"m": "Male", "f": "Female"}
state_map = {"NY": "New York", "CA": "California"}
trainable = Map(columns=[
replace(it.gender, gender_map),
replace(it.state, state_map)
])
trained = trainable.fit(sdf)
transformed_df = trained.transform(sdf)
self.assertEqual(
(transformed_df.count(), len(transformed_df.columns)), (5, 3))
self.assertEqual(transformed_df.head()[0], "Male")
self.assertEqual(transformed_df.head()[1], "New York")