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Remove fico and update example, tutorial, and READMEs #3

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0d23c12
remove the FICO dataset
Jan 11, 2023
87774b3
update example.py, tutorial.ipynb, and both READMEs
chudizhong Jan 11, 2023
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107 changes: 60 additions & 47 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -288,119 +288,132 @@ import numpy as np
import time
import pathlib
from sklearn.ensemble import GradientBoostingClassifier
from model.threshold_guess import compute_thresholds
from sklearn.model_selection import train_test_split
from model.threshold_guess import compute_thresholds, cut
from model.gosdt import GOSDT

# read the dataset
df = pd.read_csv("experiments/datasets/fico.csv")
X, y = df.iloc[:,:-1].values, df.iloc[:,-1].values
df = pd.read_csv("../experiments/datasets/compas.csv")
X, y = df.iloc[:,:-1], df.iloc[:,-1]
h = df.columns[:-1]

# train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2021)
print("X train shape:{}, X test shape:{}".format(X_train.shape, X_test.shape))

# GBDT parameters for threshold and lower bound guesses
n_est = 40
max_depth = 1

# guess thresholds
X = pd.DataFrame(X, columns=h)
print("X:", X.shape)
print("y:",y.shape)
X_train, thresholds, header, threshold_guess_time = compute_thresholds(X, y, n_est, max_depth)
y_train = pd.DataFrame(y)
# 1.guess thresholds
X_train = pd.DataFrame(X_train, columns=h)
X_test = pd.DataFrame(X_test, columns=h)
X_train_guessed, thresholds, header, threshold_guess_time = compute_thresholds(X_train.copy(), y_train, n_est, max_depth)
X_test_guessed = cut(X_test.copy(), thresholds)
X_test_guessed = X_test_guessed[header]
print("After guessing, X train shape:{}, X test shape:{}".format(X_train_guessed.shape, X_test_guessed.shape))
print("train set column names == test set column names: {}".format(list(X_train_guessed.columns)==list(X_test_guessed.columns)))


# guess lower bound

# 2.guess lower bound
import time
start_time = time.perf_counter()
clf = GradientBoostingClassifier(n_estimators=n_est, max_depth=max_depth, random_state=42)
clf.fit(X_train, y_train.values.flatten())
warm_labels = clf.predict(X_train)
clf.fit(X_train_guessed, y_train.values.flatten())
warm_labels = clf.predict(X_train_guessed)

elapsed_time = time.perf_counter() - start_time

lb_time = elapsed_time

# save the labels from lower bound guesses as a tmp file and return the path to it.
# save the labels as a tmp file and return the path to it.
labelsdir = pathlib.Path('/tmp/warm_lb_labels')
labelsdir.mkdir(exist_ok=True, parents=True)

labelpath = labelsdir / 'warm_label.tmp'
labelpath = str(labelpath)
pd.DataFrame(warm_labels, columns=["class_labels"]).to_csv(labelpath, header="class_labels",index=None)
pd.DataFrame(warm_labels).to_csv(labelpath, header="class_labels",index=None)



# train GOSDT model
# 3.train GOSDT model
config = {
"regularization": 0.001,
"depth_budget": 5,
"depth_budget": 6,
"time_limit": 60,
"warm_LB": True,
"path_to_labels": labelpath,
"time_limit": 60,
"similar_support": False
"similar_support": False,
}

model = GOSDT(config)

model.fit(X_train, y_train)
model.fit(X_train_guessed, y_train)

print("evaluate the model, extracting tree and scores", flush=True)

# get the results
train_acc = model.score(X_train, y_train)
# 4.get the results
train_acc = model.score(X_train_guessed, y_train)
test_acc = model.score(X_test_guessed, y_test)
n_leaves = model.leaves()
n_nodes = model.nodes()
time = model.utime

print("Model training time: {}".format(time))
print("Training accuracy: {}".format(train_acc))
print("Test accuracy: {}".format(test_acc))
print("# of leaves: {}".format(n_leaves))
print(model.tree)
```

**Output**

```
X: (10459, 23)
y: (10459,)
X train shape:(5525, 7), X test shape:(1382, 7)
After guessing, X train shape:(5525, 17), X test shape:(1382, 17)
train set column names == test set column names: True
gosdt reported successful execution
training completed. 1.658/0.098/1.756 (user, system, wall), mem=364 MB
bounds: [0.290914..0.290914] (0.000000) loss=0.282914, iterations=13569
training completed. 0.000/0.000/0.709 (user, system, wall), mem=0 MB
bounds: [0.325914..0.325914] (0.000000) loss=0.318914, iterations=12342
evaluate the model, extracting tree and scores
Model training time: 1.6584229469299316
Training accuracy: 0.7170857634573095
# of leaves: 8
if ExternalRiskEstimate<=67.5 = 1 and MSinceMostRecentInqexcl7days<=-7.5 = 1 then:
Model training time: 0.0
Training accuracy: 0.6810859728506787
Test accuracy: 0.6903039073806078
# of leaves: 7
if age<=21.5 = 1 then:
predicted class: 1
misclassification penalty: 0.027
misclassification penalty: 0.026
complexity penalty: 0.001

else if ExternalRiskEstimate<=67.5 != 1 and MSinceMostRecentInqexcl7days<=-7.5 = 1 then:
else if age<=21.5 != 1 and age<=27.5 = 1 and priors_count<=1.5 = 1 then:
predicted class: 0
misclassification penalty: 0.006
misclassification penalty: 0.053
complexity penalty: 0.001

else if ExternalRiskEstimate<=74.5 = 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 = 1 and PercentTradesWBalance<=80.5 = 1 then:
else if age<=21.5 != 1 and age<=27.5 = 1 and priors_count<=1.5 != 1 then:
predicted class: 1
misclassification penalty: 0.071
misclassification penalty: 0.04
complexity penalty: 0.001

else if ExternalRiskEstimate<=74.5 != 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 = 1 and PercentTradesWBalance<=80.5 = 1 then:
else if age<=21.5 != 1 and age<=27.5 != 1 and age<=38.5 = 1 and priors_count<=3.5 = 1 then:
predicted class: 0
misclassification penalty: 0.061
misclassification penalty: 0.068
complexity penalty: 0.001

else if ExternalRiskEstimate<=78.5 = 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 = 1 and PercentTradesWBalance<=80.5 != 1 then:
else if age<=21.5 != 1 and age<=27.5 != 1 and age<=38.5 = 1 and priors_count<=3.5 != 1 then:
predicted class: 1
misclassification penalty: 0.033
misclassification penalty: 0.045
complexity penalty: 0.001

else if ExternalRiskEstimate<=78.5 != 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 = 1 and PercentTradesWBalance<=80.5 != 1 then:
else if age<=21.5 != 1 and age<=27.5 != 1 and age<=38.5 != 1 and priors_count<=7.5 = 1 then:
predicted class: 0
misclassification penalty: 0.005
misclassification penalty: 0.067
complexity penalty: 0.001

else if ExternalRiskEstimate<=67.5 = 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 != 1 then:
else if age<=21.5 != 1 and age<=27.5 != 1 and age<=38.5 != 1 and priors_count<=7.5 != 1 then:
predicted class: 1
misclassification penalty: 0.026
complexity penalty: 0.001

else if ExternalRiskEstimate<=67.5 != 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 != 1 then:
predicted class: 0
misclassification penalty: 0.054
misclassification penalty: 0.021
complexity penalty: 0.001
```

Expand Down
107 changes: 60 additions & 47 deletions README_PyPI.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -280,119 +280,132 @@ import numpy as np
import time
import pathlib
from sklearn.ensemble import GradientBoostingClassifier
from model.threshold_guess import compute_thresholds
from sklearn.model_selection import train_test_split
from model.threshold_guess import compute_thresholds, cut
from model.gosdt import GOSDT

# read the dataset
df = pd.read_csv("experiments/datasets/fico.csv")
X, y = df.iloc[:,:-1].values, df.iloc[:,-1].values
df = pd.read_csv("../experiments/datasets/compas.csv")
X, y = df.iloc[:,:-1], df.iloc[:,-1]
h = df.columns[:-1]

# train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2021)
print("X train shape:{}, X test shape:{}".format(X_train.shape, X_test.shape))

# GBDT parameters for threshold and lower bound guesses
n_est = 40
max_depth = 1

# guess thresholds
X = pd.DataFrame(X, columns=h)
print("X:", X.shape)
print("y:",y.shape)
X_train, thresholds, header, threshold_guess_time = compute_thresholds(X, y, n_est, max_depth)
y_train = pd.DataFrame(y)
# 1.guess thresholds
X_train = pd.DataFrame(X_train, columns=h)
X_test = pd.DataFrame(X_test, columns=h)
X_train_guessed, thresholds, header, threshold_guess_time = compute_thresholds(X_train.copy(), y_train, n_est, max_depth)
X_test_guessed = cut(X_test.copy(), thresholds)
X_test_guessed = X_test_guessed[header]
print("After guessing, X train shape:{}, X test shape:{}".format(X_train_guessed.shape, X_test_guessed.shape))
print("train set column names == test set column names: {}".format(list(X_train_guessed.columns)==list(X_test_guessed.columns)))


# guess lower bound

# 2.guess lower bound
import time
start_time = time.perf_counter()
clf = GradientBoostingClassifier(n_estimators=n_est, max_depth=max_depth, random_state=42)
clf.fit(X_train, y_train.values.flatten())
warm_labels = clf.predict(X_train)
clf.fit(X_train_guessed, y_train.values.flatten())
warm_labels = clf.predict(X_train_guessed)

elapsed_time = time.perf_counter() - start_time

lb_time = elapsed_time

# save the labels from lower bound guesses as a tmp file and return the path to it.
# save the labels as a tmp file and return the path to it.
labelsdir = pathlib.Path('/tmp/warm_lb_labels')
labelsdir.mkdir(exist_ok=True, parents=True)

labelpath = labelsdir / 'warm_label.tmp'
labelpath = str(labelpath)
pd.DataFrame(warm_labels, columns=["class_labels"]).to_csv(labelpath, header="class_labels",index=None)
pd.DataFrame(warm_labels).to_csv(labelpath, header="class_labels",index=None)



# train GOSDT model
# 3.train GOSDT model
config = {
"regularization": 0.001,
"depth_budget": 5,
"depth_budget": 6,
"time_limit": 60,
"warm_LB": True,
"path_to_labels": labelpath,
"time_limit": 60,
"similar_support": False
"similar_support": False,
}

model = GOSDT(config)

model.fit(X_train, y_train)
model.fit(X_train_guessed, y_train)

print("evaluate the model, extracting tree and scores", flush=True)

# get the results
train_acc = model.score(X_train, y_train)
# 4.get the results
train_acc = model.score(X_train_guessed, y_train)
test_acc = model.score(X_test_guessed, y_test)
n_leaves = model.leaves()
n_nodes = model.nodes()
time = model.utime

print("Model training time: {}".format(time))
print("Training accuracy: {}".format(train_acc))
print("Test accuracy: {}".format(test_acc))
print("# of leaves: {}".format(n_leaves))
print(model.tree)
```

**Output**

```
X: (10459, 23)
y: (10459,)
X train shape:(5525, 7), X test shape:(1382, 7)
After guessing, X train shape:(5525, 17), X test shape:(1382, 17)
train set column names == test set column names: True
gosdt reported successful execution
training completed. 1.658/0.098/1.756 (user, system, wall), mem=364 MB
bounds: [0.290914..0.290914] (0.000000) loss=0.282914, iterations=13569
training completed. 0.000/0.000/0.709 (user, system, wall), mem=0 MB
bounds: [0.325914..0.325914] (0.000000) loss=0.318914, iterations=12342
evaluate the model, extracting tree and scores
Model training time: 1.6584229469299316
Training accuracy: 0.7170857634573095
# of leaves: 8
if ExternalRiskEstimate<=67.5 = 1 and MSinceMostRecentInqexcl7days<=-7.5 = 1 then:
Model training time: 0.0
Training accuracy: 0.6810859728506787
Test accuracy: 0.6903039073806078
# of leaves: 7
if age<=21.5 = 1 then:
predicted class: 1
misclassification penalty: 0.027
misclassification penalty: 0.026
complexity penalty: 0.001

else if ExternalRiskEstimate<=67.5 != 1 and MSinceMostRecentInqexcl7days<=-7.5 = 1 then:
else if age<=21.5 != 1 and age<=27.5 = 1 and priors_count<=1.5 = 1 then:
predicted class: 0
misclassification penalty: 0.006
misclassification penalty: 0.053
complexity penalty: 0.001

else if ExternalRiskEstimate<=74.5 = 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 = 1 and PercentTradesWBalance<=80.5 = 1 then:
else if age<=21.5 != 1 and age<=27.5 = 1 and priors_count<=1.5 != 1 then:
predicted class: 1
misclassification penalty: 0.071
misclassification penalty: 0.04
complexity penalty: 0.001

else if ExternalRiskEstimate<=74.5 != 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 = 1 and PercentTradesWBalance<=80.5 = 1 then:
else if age<=21.5 != 1 and age<=27.5 != 1 and age<=38.5 = 1 and priors_count<=3.5 = 1 then:
predicted class: 0
misclassification penalty: 0.061
misclassification penalty: 0.068
complexity penalty: 0.001

else if ExternalRiskEstimate<=78.5 = 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 = 1 and PercentTradesWBalance<=80.5 != 1 then:
else if age<=21.5 != 1 and age<=27.5 != 1 and age<=38.5 = 1 and priors_count<=3.5 != 1 then:
predicted class: 1
misclassification penalty: 0.033
misclassification penalty: 0.045
complexity penalty: 0.001

else if ExternalRiskEstimate<=78.5 != 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 = 1 and PercentTradesWBalance<=80.5 != 1 then:
else if age<=21.5 != 1 and age<=27.5 != 1 and age<=38.5 != 1 and priors_count<=7.5 = 1 then:
predicted class: 0
misclassification penalty: 0.005
misclassification penalty: 0.067
complexity penalty: 0.001

else if ExternalRiskEstimate<=67.5 = 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 != 1 then:
else if age<=21.5 != 1 and age<=27.5 != 1 and age<=38.5 != 1 and priors_count<=7.5 != 1 then:
predicted class: 1
misclassification penalty: 0.026
complexity penalty: 0.001

else if ExternalRiskEstimate<=67.5 != 1 and MSinceMostRecentInqexcl7days<=-7.5 != 1 and MSinceMostRecentInqexcl7days<=0.5 != 1 then:
predicted class: 0
misclassification penalty: 0.054
misclassification penalty: 0.021
complexity penalty: 0.001
```

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