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746 tests vdataframe math and rolling (#776)
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* UT code for math and rolling functions

* moved reusable code to init file

* black format chnage for machine_learning/memmodel/naive_bayes.py
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@vikash018
vikash018 authored Oct 31, 2023
1 parent 92b5fc2 commit 7ba6a89
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71 changes: 71 additions & 0 deletions verticapy/tests_new/__init__.py
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See the License for the specific language governing
permissions and limitations under the License.
"""
from collections import namedtuple

AggregateFun = namedtuple("AggregateFun", ["vpy", "py"])
functions = {
"aad": [
"vpy_data.aad()",
"np.absolute(py_data - py_data.mean(numeric_only=True)).mean(numeric_only=True)",
],
"count": ["vpy_data.count()", "py_data.count()"],
"cvar": [
"vpy_data.cvar()",
"py_data[py_data >= py_data.quantile(0.95, numeric_only=True)].mean(numeric_only=True)",
],
"iqr": [
"vpy_data.iqr()",
"py_data.quantile(0.75, numeric_only=True) - py_data.quantile(0.25, numeric_only=True)",
],
"kurt": ["vpy_data.kurt()", "py_data.kurt(numeric_only=True)"],
"kurtosis": [
"vpy_data.kurtosis()",
"py_data.kurtosis(numeric_only=True)",
],
"jb": ["vpy_data.jb()", "jarque_bera(py_data, nan_policy='omit').statistic"],
"mad": [
"vpy_data.mad()",
"median_abs_deviation(py_data, nan_policy='omit')",
],
"max": ["vpy_data.max()", "py_data.max(numeric_only=True)"],
"mean": ["vpy_data.mean()", "py_data.mean(numeric_only=True)"],
"avg": ["vpy_data.avg()", "py_data.mean(numeric_only=True)"],
"median": ["vpy_data.median()", "py_data.median(numeric_only=True)"],
"min": ["vpy_data.min()", "py_data.min(numeric_only=True)"],
"mode": ["vpy_data.mode()", "py_data.mode(numeric_only=True, dropna=False).values"],
"percent": ["vpy_data.percent()", "py_data.count()/len(py_data)*100"],
"quantile": [
"vpy_data.quantile(q=[0.2, 0.5])",
"py_data.quantile(q=[0.2, 0.5],numeric_only=True).values",
],
"10%": ["vpy_data.q10()", "py_data.quantile(0.1, numeric_only=True)"],
"90%": ["vpy_data.q90", "py_data.quantile(0.9, numeric_only=True)"],
"prod": ["vpy_data.prod()", "py_data.prod(numeric_only=True)"],
"product": ["vpy_data.product()", "py_data.product(numeric_only=True)"],
"range": [
"vpy_data.range()",
"py_data.max(numeric_only=True) - py_data.min(numeric_only=True)",
],
"sem": ["vpy_data.sem()", "py_data.sem(numeric_only=True)"],
"skew": ["vpy_data.skew()", "py_data.skew(numeric_only=True)"],
"skewness": ["vpy_data.skewness()", "py_data.skew(numeric_only=True)"],
"sum": ["vpy_data.sum()", "py_data.sum(numeric_only=True)"],
"std": ["vpy_data.std()", "py_data.std(numeric_only=True)"],
"stddev": ["vpy_data.stddev()", "py_data.std(numeric_only=True)"],
"topk": ["vpy_data.topk(k=3)", "py_data.value_counts(dropna=False)"],
"top1": ["vpy_data.topk(k=1)", "py_data.value_counts(dropna=False).index[0]"],
"top1_percent": [
"vpy_data.top1_percent()",
"py_data.value_counts(dropna=False).iloc[0]/len(py_data)*100",
],
"nunique": ["vpy_data.nunique(approx=False)", "py_data.nunique()"],
"unique": ["vpy_data.nunique(approx=False)", "py_data.nunique()"],
"var": ["vpy_data.var()", "py_data.var(numeric_only=True)"],
"variance": ["vpy_data.variance()", "py_data.var(numeric_only=True)"],
"value_counts": [
"vpy_data.value_counts()",
"py_data.value_counts(dropna=False)",
],
"distinct": [
"vpy_data.distinct()",
"py_data.unique()",
],
}
73 changes: 1 addition & 72 deletions verticapy/tests_new/core/vdataframe/test_agg.py
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See the License for the specific language governing
permissions and limitations under the License.
"""
from collections import namedtuple
from contextlib import nullcontext as does_not_raise
import pytest
import numpy as np
from scipy.stats import median_abs_deviation, jarque_bera
from verticapy.errors import MissingColumn
import verticapy as vp
from verticapy.tests_new.core.vdataframe import REL_TOLERANCE, ABS_TOLERANCE

AggregateFun = namedtuple("AggregateFun", ["vpy", "py"])
functions = {
"aad": [
"vpy_data.aad()",
"np.absolute(py_data - py_data.mean(numeric_only=True)).mean(numeric_only=True)",
],
"count": ["vpy_data.count()", "py_data.count()"],
"cvar": [
"vpy_data.cvar()",
"py_data[py_data >= py_data.quantile(0.95, numeric_only=True)].mean(numeric_only=True)",
],
"iqr": [
"vpy_data.iqr()",
"py_data.quantile(0.75, numeric_only=True) - py_data.quantile(0.25, numeric_only=True)",
],
"kurt": ["vpy_data.kurt()", "py_data.kurt(numeric_only=True)"],
"kurtosis": [
"vpy_data.kurtosis()",
"py_data.kurtosis(numeric_only=True)",
],
"jb": ["vpy_data.jb()", "jarque_bera(py_data, nan_policy='omit').statistic"],
"mad": [
"vpy_data.mad()",
"median_abs_deviation(py_data, nan_policy='omit')",
],
"max": ["vpy_data.max()", "py_data.max(numeric_only=True)"],
"mean": ["vpy_data.mean()", "py_data.mean(numeric_only=True)"],
"avg": ["vpy_data.avg()", "py_data.mean(numeric_only=True)"],
"median": ["vpy_data.median()", "py_data.median(numeric_only=True)"],
"min": ["vpy_data.min()", "py_data.min(numeric_only=True)"],
"mode": ["vpy_data.mode()", "py_data.mode(numeric_only=True, dropna=False).values"],
"percent": ["vpy_data.percent()", "py_data.count()/len(py_data)*100"],
"quantile": [
"vpy_data.quantile(q=[0.2, 0.5])",
"py_data.quantile(q=[0.2, 0.5],numeric_only=True).values",
],
"10%": ["vpy_data.q10()", "py_data.quantile(0.1, numeric_only=True)"],
"90%": ["vpy_data.q90", "py_data.quantile(0.9, numeric_only=True)"],
"prod": ["vpy_data.prod()", "py_data.prod(numeric_only=True)"],
"product": ["vpy_data.product()", "py_data.product(numeric_only=True)"],
"range": [
"vpy_data.range()",
"py_data.max(numeric_only=True) - py_data.min(numeric_only=True)",
],
"sem": ["vpy_data.sem()", "py_data.sem(numeric_only=True)"],
"skew": ["vpy_data.skew()", "py_data.skew(numeric_only=True)"],
"skewness": ["vpy_data.skewness()", "py_data.skew(numeric_only=True)"],
"sum": ["vpy_data.sum()", "py_data.sum(numeric_only=True)"],
"std": ["vpy_data.std()", "py_data.std(numeric_only=True)"],
"stddev": ["vpy_data.stddev()", "py_data.std(numeric_only=True)"],
"topk": ["vpy_data.topk(k=3)", "py_data.value_counts(dropna=False)"],
"top1": ["vpy_data.topk(k=1)", "py_data.value_counts(dropna=False).index[0]"],
"top1_percent": [
"vpy_data.top1_percent()",
"py_data.value_counts(dropna=False).iloc[0]/len(py_data)*100",
],
"nunique": ["vpy_data.nunique(approx=False)", "py_data.nunique()"],
"unique": ["vpy_data.nunique(approx=False)", "py_data.nunique()"],
"var": ["vpy_data.var()", "py_data.var(numeric_only=True)"],
"variance": ["vpy_data.variance()", "py_data.var(numeric_only=True)"],
"value_counts": [
"vpy_data.value_counts()",
"py_data.value_counts(dropna=False)",
],
"distinct": [
"vpy_data.distinct()",
"py_data.unique()",
],
}
from verticapy.tests_new import functions, AggregateFun


class TestAgg:
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