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Merged
MadhuTangudu merged 4 commits into from
Sep 28, 2021
Merged

feature_04_05_testing #14

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d7c73bb
v0.4.0
bdiptesh Sep 27, 2021
38787df
v0.4.0
MadhuTangudu Sep 27, 2021
352c968
v0.4.0
bdiptesh Sep 27, 2021
af7cfa0
v0.4.0
bdiptesh Sep 27, 2021
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19 changes: 10 additions & 9 deletions logs/cov.out
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Original file line number Diff line number Diff line change
@@ -1,9 +1,10 @@
Name Stmts Miss Cover Missing
-----------------------------------------------------
mllib/__init__.py 7 0 100%
mllib/lib/__init__.py 7 0 100%
mllib/lib/cluster.py 103 0 100%
mllib/lib/knn.py 70 0 100%
mllib/lib/model.py 44 0 100%
-----------------------------------------------------
TOTAL 231 0 100%
Name Stmts Miss Cover Missing
-----------------------------------------------------------------------------------------
/media/ph33r/Data/Project/mllib/Git/mllib/__init__.py 7 0 100%
/media/ph33r/Data/Project/mllib/Git/mllib/lib/__init__.py 7 0 100%
/media/ph33r/Data/Project/mllib/Git/mllib/lib/cluster.py 103 0 100%
/media/ph33r/Data/Project/mllib/Git/mllib/lib/knn.py 70 0 100%
/media/ph33r/Data/Project/mllib/Git/mllib/lib/model.py 44 0 100%
/media/ph33r/Data/Project/mllib/Git/mllib/lib/tree.py 79 0 100%
-----------------------------------------------------------------------------------------
TOTAL 310 0 100%
2 changes: 1 addition & 1 deletion logs/pip.out
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@@ -1 +1 @@
./bin/run_tests.sh: line 78: pipreqs: command not found
INFO: Successfully saved requirements file in /media/ph33r/Data/Project/mllib/Git/requirements.txt
9 changes: 0 additions & 9 deletions logs/pylint/lib-glmnet_ts-py.out
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This file was deleted.

9 changes: 9 additions & 0 deletions logs/pylint/lib-tree-py.out
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Original file line number Diff line number Diff line change
@@ -0,0 +1,9 @@
************* Module mllib.lib.tree
tree.py:73:45: I1101: Module 'metrics' has no 'rsq' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member)
tree.py:74:45: I1101: Module 'metrics' has no 'mae' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member)
tree.py:75:46: I1101: Module 'metrics' has no 'mape' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member)
tree.py:76:46: I1101: Module 'metrics' has no 'rmse' member, but source is unavailable. Consider adding this module to extension-pkg-whitelist if you want to perform analysis based on run-time introspection of living objects. (c-extension-no-member)

--------------------------------------------------------------------
Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00)

4 changes: 0 additions & 4 deletions logs/pylint/tests-test_glmnet_ts-py.out
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0 logs/pylint/lib-glmnet-py.out → logs/pylint/tests-test_tree-py.out
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24 changes: 24 additions & 0 deletions mllib/__main__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,8 @@
from lib.cluster import Cluster # noqa: F841
from lib.model import GLMNet # noqa: F841
from lib.knn import KNN # noqa: F841
from lib.tree import RandomForest # noqa: F841
from lib.tree import XGBoost # noqa: F841

# =============================================================================
# --- DO NOT CHANGE ANYTHING FROM HERE
Expand Down Expand Up @@ -92,6 +94,28 @@
df_ip = pd.read_csv(path + "input/iris.csv")
mod = KNN(df_ip, "y", ["x1", "x2", "x3", "x4"], method="classify")
print("\nKNN\n")
for k, v in mod.model_summary.items():
print(k, str(v).rjust(69 - len(k)))
print(elapsed_time("Time", start_t),
sep="\n")
# --- Random forest
start_t = time.time_ns()
df_ip = pd.read_csv(path + "input/iris.csv")
x_var = ["x1", "x2", "x3", "x4"]
y_var = "y"
mod = RandomForest(df_ip, y_var, x_var, method="classify")
print("\nRandom forest\n")
for k, v in mod.model_summary.items():
print(k, str(v).rjust(69 - len(k)))
print(elapsed_time("Time", start_t),
sep="\n")
# --- XGBoost
start_t = time.time_ns()
df_ip = pd.read_csv(path + "input/iris.csv")
x_var = ["x1", "x2", "x3", "x4"]
y_var = "y"
mod = XGBoost(df_ip, y_var, x_var, method="classify")
print("\nXGBoost\n")
for k, v in mod.model_summary.items():
print(k, str(v).rjust(69 - len(k)))
print(elapsed_time("Time", start_t),
Expand Down
8 changes: 4 additions & 4 deletions mllib/lib/knn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,7 +104,7 @@ class KNN():
Example
-------
>>> mod = KNN(df=df_ip, y_var="y", x_var=["x1", "x2", "x3"])
>>> df_op = mod.predict(df_predict)
>>> df_op = mod.predict(x_predict)

"""

Expand Down Expand Up @@ -189,7 +189,7 @@ def _compute_metrics(self):
for key in model_summary}
self.model_summary = model_summary

def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame:
def predict(self, x_predict: pd.DataFrame) -> pd.DataFrame:
"""Predict y_var/target variable.

Parameters
Expand All @@ -205,8 +205,8 @@ def predict(self, df_predict: pd.DataFrame) -> pd.DataFrame:
Pandas dataframe containing predicted `y_var` and `x_var`.

"""
df_op = df_predict.copy(deep=True)
df_predict = pd.get_dummies(df_predict)
df_op = x_predict.copy(deep=True)
df_predict = pd.get_dummies(x_predict)
df_predict_tmp = pd.DataFrame(columns=self.x_var)
df_predict = pd.concat([df_predict_tmp, df_predict])
df_predict = df_predict.fillna(0)
Expand Down
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