def extract_datepart(df: pyspark.sql.DataFrame,
dt_col: str,
to_extract: str,
drop: bool = False) -> pyspark.sql.DataFrame:
"""
Base function for extracting dateparts. Used in less abstracted functions.
Parameters
----------
df : pyspark.sql.DataFrame
Base dataframe which contains ``dt_col`` column for extracting ``to_extract``.
dt_col : str
Name of date column to extract ``to_extract`` from.
to_extract : str
TODO
drop : bool
Whether or not to drop dt_col after extraction (default is False).
Returns
-------
df : pyspark.sql.DataFrame
df with ``to_extract`` column, optionally without original ``dt_col`` column.
"""
df = df.withColumn(to_extract, getattr(F, to_extract)(F.col(dt_col)))
if drop:
df = df.drop(dt_col)
return df
def lookup_columns(df: pyspark.sql.DataFrame,
vocab: Dict[str, List[Any]]) -> pyspark.sql.DataFrame:
def lookup(mapping):
def fn(v):
return mapping.index(v)
return F.udf(fn, returnType=T.IntegerType())
for col, mapping in vocab.items():
df = df.withColumn(col, lookup(mapping)(df[col]))
return df
def prepare_google_trend(
google_trend_csv: pyspark.sql.DataFrame, ) -> pyspark.sql.DataFrame:
google_trend_all = google_trend_csv.withColumn(
"Date",
F.regexp_extract(google_trend_csv.week, "(.*?) -", 1)).withColumn(
"State",
F.regexp_extract(google_trend_csv.file, "Rossmann_DE_(.*)", 1))
# map state NI -> HB,NI to align with other data sources
google_trend_all = google_trend_all.withColumn(
"State",
F.when(google_trend_all.State == "NI",
"HB,NI").otherwise(google_trend_all.State),
)
# expand dates
return expand_date(google_trend_all)
def cast(df: pyspark.sql.DataFrame, col_name: str,
dtype: str) -> pyspark.sql.DataFrame:
return df.withColumn(col_name, F.col(col_name).cast(dtype))
def cast_columns(df: pyspark.sql.DataFrame,
cols: List[str]) -> pyspark.sql.DataFrame:
for col in cols:
df = df.withColumn(col,
F.coalesce(df[col].cast(T.FloatType()), F.lit(0.0)))
return df
def prepare_df(
df: pyspark.sql.DataFrame,
store_csv: pyspark.sql.DataFrame,
store_states_csv: pyspark.sql.DataFrame,
state_names_csv: pyspark.sql.DataFrame,
google_trend_csv: pyspark.sql.DataFrame,
weather_csv: pyspark.sql.DataFrame,
) -> pyspark.sql.DataFrame:
num_rows = df.count()
# expand dates
df = expand_date(df)
# create new columns in the DataFrame by filtering out special events(promo/holiday where sales was zero or store was closed).
df = (df.withColumn("Open", df.Open != "0").withColumn(
"Promo",
df.Promo != "0").withColumn("StateHoliday",
df.StateHoliday != "0").withColumn(
"SchoolHoliday",
df.SchoolHoliday != "0"))
# merge store information
store = store_csv.join(store_states_csv, "Store")
df = df.join(store, "Store")
# merge Google Trend information
google_trend_all = prepare_google_trend(google_trend_csv)
df = df.join(google_trend_all,
["State", "Year", "Week"]).select(df["*"],
google_trend_all.trend)
# merge in Google Trend for whole Germany
google_trend_de = google_trend_all[google_trend_all.file ==
"Rossmann_DE"].withColumnRenamed(
"trend", "trend_de")
df = df.join(google_trend_de,
["Year", "Week"]).select(df["*"], google_trend_de.trend_de)
# merge weather
weather = weather_csv.join(state_names_csv,
weather_csv.file == state_names_csv.StateName)
df = df.join(weather, ["State", "Date"])
# fix null values
df = (df.withColumn(
"CompetitionOpenSinceYear",
F.coalesce(df.CompetitionOpenSinceYear, F.lit(1900)),
).withColumn(
"CompetitionOpenSinceMonth",
F.coalesce(df.CompetitionOpenSinceMonth, F.lit(1)),
).withColumn("Promo2SinceYear",
F.coalesce(df.Promo2SinceYear, F.lit(1900))).withColumn(
"Promo2SinceWeek", F.coalesce(df.Promo2SinceWeek,
F.lit(1))))
# days and months since the competition has been open, cap it to 2 years
df = df.withColumn(
"CompetitionOpenSince",
F.to_date(
F.format_string("%s-%s-15", df.CompetitionOpenSinceYear,
df.CompetitionOpenSinceMonth)),
)
df = df.withColumn(
"CompetitionDaysOpen",
F.when(
df.CompetitionOpenSinceYear > 1900,
F.greatest(
F.lit(0),
F.least(F.lit(360 * 2),
F.datediff(df.Date, df.CompetitionOpenSince)),
),
).otherwise(0),
)
df = df.withColumn("CompetitionMonthsOpen",
(df.CompetitionDaysOpen / 30).cast(T.IntegerType()))
# days and weeks of promotion, cap it to 25 weeks
df = df.withColumn(
"Promo2Since",
F.expr(
'date_add(format_string("%s-01-01", Promo2SinceYear), (cast(Promo2SinceWeek as int) - 1) * 7)'
),
)
df = df.withColumn(
"Promo2Days",
F.when(
df.Promo2SinceYear > 1900,
F.greatest(
F.lit(0),
F.least(F.lit(25 * 7), F.datediff(df.Date, df.Promo2Since))),
).otherwise(0),
)
df = df.withColumn("Promo2Weeks",
(df.Promo2Days / 7).cast(T.IntegerType()))
# ensure that no row was lost through inner joins
assert num_rows == df.count(), "lost rows in joins"
return df
def expand_date(df: pyspark.sql.DataFrame) -> pyspark.sql.DataFrame:
df = df.withColumn("Date", df.Date.cast(T.DateType()))
return (df.withColumn("Year", F.year(df.Date)).withColumn(
"Month",
F.month(df.Date)).withColumn("Week", F.weekofyear(df.Date)).withColumn(
"Day", F.dayofmonth(df.Date)))
def metrics (session: SparkSession, dataframe: pyspark.sql.DataFrame, actual: str,
predicted: str) -> pyspark.sql.DataFrame:
'''
Calculates evaluation metrics from predicted results
:param dataframe: spark.sql.dataframe with the real and predicted values
:param actual: Name of column with observed target values
:param predicted: Name of column with predicted values
:return:
'''
# Along each row are the actual values and down each column are the predicted
dataframe = dataframe.withColumn(actual, col(actual).cast('integer'))
dataframe = dataframe.withColumn(predicted, col(predicted).cast('integer'))
cm = dataframe.crosstab(actual, predicted)
cm = cm.sort(cm.columns[0], ascending=True)
# Adds missing column in case just one class was predicted
if not '0' in cm.columns:
cm = cm.withColumn('0', lit(0))
if not '1' in cm.columns:
cm = cm.withColumn('1', lit(0))
# Subsets values from confusion matrix
zero = cm.filter(cm[cm.columns[0]] == 0.0)
first_0 = zero.take(1)
one = cm.filter(cm[cm.columns[0]] == 1.0)
first_1 = one.take(1)
tn = first_0[0][1]
fp = first_0[0][2]
fn = first_1[0][1]
tp = first_1[0][2]
# Calculate metrics from values in the confussion matrix
if (tp == 0):
acc = float((tp + tn) / (tp + tn + fp + fn))
sen = 0
spe = float((tn) / (tn + fp))
prec = 0
rec = 0
f1 = 0
elif (tn == 0):
acc = float((tp + tn) / (tp + tn + fp + fn))
sen = float((tp) / (tp + fn))
spe = 0
prec = float((tp) / (tp + fp))
rec = float((tp) / (tp + fn))
f1 = 2 * float((prec * rec) / (prec + rec))
else:
acc = float((tp + tn) / (tp + tn + fp + fn))
sen = float((tp) / (tp + fn))
spe = float((tn) / (tn + fp))
prec = float((tp) / (tp + fp))
rec = float((tp) / (tp + fn))
f1 = 2 * float((prec * rec) / (prec + rec))
# Print results
print('Confusion Matrix and Statistics: \n')
cm.show()
print('True Positives:', tp)
print('True Negatives:', tn)
print('False Positives:', fp)
print('False Negatives:', fn)
print('Total:', dataframe.count(), '\n')
print('Accuracy: {0:.2f}'.format(acc))
print('Sensitivity: {0:.2f}'.format(sen))
print('Specificity: {0:.2f}'.format(spe))
print('Precision: {0:.2f}'.format(prec))
print('Recall: {0:.2f}'.format(rec))
print('F1-score: {0:.2f}'.format(f1))
# Create spark dataframe with results
l = [(acc, sen, spe, prec, rec, f1)]
df = session.createDataFrame(l, ['Accuracy', 'Sensitivity', 'Specificity', 'Precision', 'Recall', 'F1'])
return df
