add model_trained.joblib lighter

.gitignore

@@ -1,7 +1,6 @@
 aws_keys.csv
 starter/data/census.csv
 starter/data/census_cleaned.csv
-starter/model/model_trained.joblib
 
 .DS_Store
 .ipynb_checkpoints

starter/main.py

@@ -24,16 +24,22 @@ class InferenceRequest(BaseModel):
     workclass: str
     fnlgt: int
     education: str
-    education_num: int = Field(alias="education-num")
-    marital_status: str = Field(alias="marital-status")
+    # education_num: int = Field(alias="education-num")
+    # marital_status: str = Field(alias="marital-status")
+    education_num: int
+    marital_status: str
     occupation: str
     relationship: str
     race: str
     sex: str
-    capital_gain: int = Field(alias="capital-gain")
-    capital_loss: int = Field(alias="capital-loss")
-    hours_per_week: int = Field(alias="hours-per-week")
-    native_country: str = Field(alias="native-country")
+    # capital_gain: int = Field(alias="capital-gain")
+    # capital_loss: int = Field(alias="capital-loss")
+    # hours_per_week: int = Field(alias="hours-per-week")
+    # native_country: str = Field(alias="native-country")
+    capital_gain: int
+    capital_loss: int
+    hours_per_week: int
+    native_country: str
 
 
 @app.get('/')
@@ -42,30 +48,31 @@ async def welcome():
 
 
 @app.post("/items")
-async def create_item(item):
+async def create_item(item: dict):
     return item
 
 
 @app.post('/predict')
-async def get_prediction(request_data: InferenceRequest = Body(
-    ...,
-    example={
-        "age": 39,
-        "workclass": "State-gov",
-        "fnlgt": 77516,
-        "education": "Bachelors",
-        "education-num": 13,
-        "marital-status": "Never-married",
-        "occupation": "Adm-clerical",
-        "relationship": "Not-in-family",
-        "race": "White",
-        "sex": "Male",
-        "capital-gain": 2174,
-        "capital-loss": 0,
-        "hours-per-week": 40,
-        "native-country": "United-States"
-    }
-)):
+async def get_prediction(request_data: InferenceRequest):
+# async def get_prediction(request_data: InferenceRequest = Body(
+#     ...,
+#     example={
+#         "age": 39,
+#         "workclass": "State-gov",
+#         "fnlgt": 77516,
+#         "education": "Bachelors",
+#         "education-num": 13,
+#         "marital-status": "Never-married",
+#         "occupation": "Adm-clerical",
+#         "relationship": "Not-in-family",
+#         "race": "White",
+#         "sex": "Male",
+#         "capital-gain": 2174,
+#         "capital-loss": 0,
+#         "hours-per-week": 40,
+#         "native-country": "United-States"
+#     }
+# )):
     cwd_p = os.getcwd()
     trained_model = joblib.load(f"{cwd_p}/starter/model/model_trained.joblib")
     encoder = joblib.load(f"{cwd_p}/starter/model/encoder.joblib")

starter/model/slice_output.txt

@@ -1,99 +1,99 @@
 feature,value,precision,recall,fbeta_score
-workclass,State-gov,0.9465875370919882,0.9273255813953488,0.9368575624082233
-workclass,Self-emp-not-inc,0.9574155653450808,0.9131652661064426,0.9347670250896057
-workclass,Private,0.9503784693019344,0.9269893355209188,0.9385382059800663
-workclass,Federal-gov,0.957983193277311,0.936986301369863,0.9473684210526316
-workclass,Local-gov,0.9656357388316151,0.922824302134647,0.943744752308984
-workclass,Self-emp-inc,0.9490131578947368,0.9616666666666667,0.955298013245033
+workclass,State-gov,0.9339080459770115,0.9447674418604651,0.9393063583815029
+workclass,Self-emp-not-inc,0.9560117302052786,0.9131652661064426,0.9340974212034385
+workclass,Private,0.9535229202037352,0.9214520098441346,0.9372131831455988
+workclass,Federal-gov,0.9482288828337875,0.9534246575342465,0.9508196721311476
+workclass,Local-gov,0.9330065359477124,0.9376026272577996,0.9352989352989352
+workclass,Self-emp-inc,0.964824120603015,0.96,0.9624060150375939
 workclass,Without-pay,1.0,1.0,1.0
-education,Bachelors,0.9535104364326376,0.9454374412041392,0.949456778460085
-education,HS-grad,0.9309021113243762,0.8998144712430427,0.9150943396226415
-education,11th,1.0,0.8813559322033898,0.936936936936937
-education,Masters,0.9608269858541894,0.9618736383442266,0.9613500272182908
-education,9th,1.0,0.92,0.9583333333333334
-education,Some-college,0.9544025157232704,0.9086826347305389,0.9309815950920245
-education,Assoc-acdm,0.9545454545454546,0.90234375,0.927710843373494
-education,7th-8th,0.9333333333333333,0.8,0.8615384615384616
-education,Doctorate,0.9713261648745519,0.9678571428571429,0.9695885509838998
-education,Assoc-voc,0.950920245398773,0.9011627906976745,0.9253731343283582
-education,Prof-school,0.973170731707317,0.9827586206896551,0.9779411764705881
-education,5th-6th,1.0,1.0,1.0
-education,10th,1.0,0.9322033898305084,0.9649122807017544
+education,Bachelors,0.9478138222849083,0.9482596425211665,0.9480366799905948
+education,HS-grad,0.9447368421052632,0.8880643166357453,0.9155243863563916
+education,11th,0.9777777777777777,0.7457627118644068,0.8461538461538463
+education,Masters,0.9759036144578314,0.9705882352941176,0.9732386673948662
+education,9th,1.0,0.72,0.8372093023255813
+education,Some-college,0.9393706830391404,0.9161676646706587,0.9276241000378931
+education,Assoc-acdm,0.9473684210526315,0.9140625,0.9304174950298211
+education,7th-8th,1.0,0.8857142857142857,0.9393939393939393
+education,Doctorate,0.968421052631579,0.9857142857142858,0.976991150442478
+education,Assoc-voc,0.9395770392749244,0.9040697674418605,0.9214814814814815
+education,Prof-school,0.9754299754299754,0.9778325123152709,0.976629766297663
+education,5th-6th,1.0,0.9166666666666666,0.9565217391304348
+education,10th,0.92,0.7796610169491526,0.8440366972477064
 education,Preschool,1.0,1.0,1.0
-education,12th,0.9615384615384616,0.8620689655172413,0.9090909090909091
-education,1st-4th,1.0,0.6666666666666666,0.8
-marital-status,Never-married,0.9830917874396136,0.8659574468085106,0.920814479638009
-marital-status,Married-civ-spouse,0.9476168003775366,0.9413970932958274,0.9444967074317968
-marital-status,Divorced,0.9768637532133676,0.8407079646017699,0.9036860879904874
-marital-status,Married-spouse-absent,1.0,0.9032258064516129,0.9491525423728813
-marital-status,Separated,1.0,0.8484848484848485,0.9180327868852458
-marital-status,Married-AF-spouse,1.0,0.9,0.9473684210526316
-marital-status,Widowed,1.0,0.85,0.9189189189189189
-occupation,Adm-clerical,0.9375,0.9036144578313253,0.920245398773006
-occupation,Exec-managerial,0.9503328213005633,0.9581827568404749,0.9542416452442161
-occupation,Handlers-cleaners,0.9078947368421053,0.8313253012048193,0.8679245283018868
-occupation,Prof-specialty,0.9642451759364359,0.9381557150745444,0.9510215505177722
-occupation,Other-service,0.991304347826087,0.8636363636363636,0.923076923076923
-occupation,Sales,0.9518828451882845,0.9381443298969072,0.9449636552440291
-occupation,Transport-moving,0.9518900343642611,0.8683385579937304,0.9081967213114753
-occupation,Farming-fishing,0.99,0.8608695652173913,0.9209302325581394
-occupation,Machine-op-inspct,0.9369747899159664,0.9102040816326531,0.9233954451345756
-occupation,Tech-support,0.9377289377289377,0.920863309352518,0.9292196007259528
-occupation,Craft-repair,0.9428571428571428,0.9085903083700441,0.9254066180594503
-occupation,Protective-serv,0.96,0.9142857142857143,0.9365853658536586
+education,12th,1.0,0.8275862068965517,0.9056603773584906
+education,1st-4th,1.0,1.0,1.0
+marital-status,Never-married,0.9782082324455206,0.8595744680851064,0.9150622876557191
+marital-status,Married-civ-spouse,0.9476182940338661,0.9357712142522269,0.9416574933165592
+marital-status,Divorced,0.9739952718676123,0.911504424778761,0.9417142857142857
+marital-status,Married-spouse-absent,1.0,0.8709677419354839,0.9310344827586207
+marital-status,Separated,1.0,0.8181818181818182,0.9
+marital-status,Married-AF-spouse,0.9090909090909091,1.0,0.9523809523809523
+marital-status,Widowed,0.9859154929577465,0.875,0.9271523178807948
+occupation,Adm-clerical,0.9386503067484663,0.9216867469879518,0.9300911854103343
+occupation,Exec-managerial,0.958656330749354,0.9576664945792462,0.9581611570247933
+occupation,Handlers-cleaners,0.9583333333333334,0.8313253012048193,0.8903225806451613
+occupation,Prof-specialty,0.9532195927352779,0.9563776918829376,0.9547960308710034
+occupation,Other-service,0.9823008849557522,0.8409090909090909,0.9061224489795919
+occupation,Sales,0.9553191489361702,0.9257731958762887,0.9403141361256545
+occupation,Transport-moving,0.9326599326599326,0.8683385579937304,0.8993506493506493
+occupation,Farming-fishing,0.9789473684210527,0.808695652173913,0.8857142857142857
+occupation,Machine-op-inspct,0.9555555555555556,0.8775510204081632,0.9148936170212767
+occupation,Tech-support,0.9416058394160584,0.9280575539568345,0.9347826086956521
+occupation,Craft-repair,0.9442467378410438,0.8766519823788547,0.9091947458595089
+occupation,Protective-serv,0.9259259259259259,0.9523809523809523,0.9389671361502347
 occupation,Armed-Forces,1.0,1.0,1.0
 occupation,Priv-house-serv,1.0,1.0,1.0
-relationship,Not-in-family,0.9806896551724138,0.8639125151883353,0.9186046511627908
-relationship,Husband,0.9492277649565063,0.9415390033456594,0.9453677510608204
-relationship,Wife,0.9328571428571428,0.9409221902017291,0.9368723098995696
-relationship,Own-child,1.0,0.875,0.9333333333333333
-relationship,Unmarried,0.9887005649717514,0.8215962441314554,0.8974358974358975
-relationship,Other-relative,1.0,0.8571428571428571,0.923076923076923
-race,White,0.9532864201227729,0.930984061997368,0.9420032549193667
-race,Black,0.9393063583815029,0.8879781420765027,0.9129213483146067
-race,Asian-Pac-Islander,0.9471544715447154,0.9395161290322581,0.9433198380566802
-race,Amer-Indian-Eskimo,1.0,0.7941176470588235,0.8852459016393442
-race,Other,0.8695652173913043,0.9523809523809523,0.909090909090909
-sex,Male,0.9525175270873167,0.9346466541588493,0.9434974747474748
-sex,Female,0.9511961722488038,0.8938848920863309,0.9216504404265182
-native-country,United-States,0.952485701715794,0.9285203716940672,0.940350369190676
-native-country,Cuba,1.0,0.96,0.9795918367346939
-native-country,Jamaica,0.875,0.7,0.7777777777777777
-native-country,India,0.8837209302325582,0.95,0.9156626506024096
-native-country,Mexico,1.0,0.9090909090909091,0.9523809523809523
-native-country,Puerto-Rico,0.8571428571428571,1.0,0.923076923076923
+relationship,Not-in-family,0.977088948787062,0.8809234507897934,0.926517571884984
+relationship,Husband,0.9477718360071301,0.9362563831660503,0.9419789175303394
+relationship,Wife,0.9437229437229437,0.9423631123919308,0.943042537851478
+relationship,Own-child,0.98,0.765625,0.8596491228070174
+relationship,Unmarried,0.9895287958115183,0.8873239436619719,0.9356435643564357
+relationship,Other-relative,0.96875,0.8857142857142857,0.9253731343283582
+race,White,0.9520814615154238,0.9296680801286737,0.9407412887475032
+race,Black,0.9463276836158192,0.9153005464480874,0.9305555555555556
+race,Asian-Pac-Islander,0.9658119658119658,0.9112903225806451,0.9377593360995851
+race,Amer-Indian-Eskimo,0.9090909090909091,0.8823529411764706,0.8955223880597014
+race,Other,0.8421052631578947,0.7619047619047619,0.8
+sex,Male,0.9505187549880287,0.9310506566604128,0.9406839902061449
+sex,Female,0.9591642924976258,0.908273381294964,0.9330254041570438
+native-country,United-States,0.9504906986963527,0.927662616154396,0.9389379250470264
+native-country,Cuba,1.0,1.0,1.0
+native-country,Jamaica,1.0,0.9,0.9473684210526316
+native-country,India,0.9743589743589743,0.95,0.9620253164556962
+native-country,Mexico,0.96875,0.9393939393939394,0.9538461538461539
+native-country,Puerto-Rico,0.9230769230769231,1.0,0.9600000000000001
 native-country,Honduras,1.0,1.0,1.0
-native-country,England,1.0,0.9333333333333333,0.9655172413793104
-native-country,Canada,0.9411764705882353,0.8888888888888888,0.9142857142857143
-native-country,Germany,0.9318181818181818,0.9318181818181818,0.9318181818181818
-native-country,Iran,1.0,0.9444444444444444,0.9714285714285714
-native-country,Philippines,0.9824561403508771,0.9333333333333333,0.9572649572649572
-native-country,Poland,0.8888888888888888,0.7272727272727273,0.7999999999999999
+native-country,England,0.9655172413793104,0.9333333333333333,0.9491525423728815
+native-country,Canada,1.0,0.9166666666666666,0.9565217391304348
+native-country,Germany,0.9333333333333333,0.9545454545454546,0.9438202247191012
+native-country,Iran,0.9473684210526315,1.0,0.972972972972973
+native-country,Philippines,0.9433962264150944,0.8333333333333334,0.8849557522123894
+native-country,Poland,1.0,0.8181818181818182,0.9
 native-country,Columbia,1.0,1.0,1.0
 native-country,Cambodia,1.0,0.8571428571428571,0.923076923076923
-native-country,Thailand,1.0,0.6666666666666666,0.8
+native-country,Thailand,1.0,1.0,1.0
 native-country,Ecuador,1.0,1.0,1.0
-native-country,Laos,1.0,1.0,1.0
-native-country,Taiwan,0.9444444444444444,0.8947368421052632,0.918918918918919
+native-country,Laos,1.0,0.5,0.6666666666666666
+native-country,Taiwan,1.0,1.0,1.0
 native-country,Haiti,1.0,0.75,0.8571428571428571
 native-country,Portugal,1.0,0.75,0.8571428571428571
 native-country,Dominican-Republic,1.0,1.0,1.0
-native-country,El-Salvador,1.0,1.0,1.0
-native-country,France,1.0,1.0,1.0
+native-country,El-Salvador,1.0,0.8888888888888888,0.9411764705882353
+native-country,France,1.0,0.9166666666666666,0.9565217391304348
 native-country,Guatemala,1.0,1.0,1.0
-native-country,Italy,0.9583333333333334,0.9583333333333334,0.9583333333333334
-native-country,China,0.9090909090909091,1.0,0.9523809523809523
-native-country,South,0.875,1.0,0.9333333333333333
-native-country,Japan,1.0,0.9130434782608695,0.9545454545454545
-native-country,Yugoslavia,1.0,0.8333333333333334,0.9090909090909091
+native-country,Italy,1.0,0.875,0.9333333333333333
+native-country,China,1.0,1.0,1.0
+native-country,South,0.9285714285714286,0.9285714285714286,0.9285714285714286
+native-country,Japan,1.0,1.0,1.0
+native-country,Yugoslavia,1.0,1.0,1.0
 native-country,Peru,1.0,1.0,1.0
 native-country,Outlying-US(Guam-USVI-etc),1.0,1.0,1.0
 native-country,Scotland,1.0,1.0,1.0
-native-country,Trinadad&Tobago,0.6666666666666666,1.0,0.8
-native-country,Greece,1.0,1.0,1.0
+native-country,Trinadad&Tobago,0.5,0.5,0.5
+native-country,Greece,0.8888888888888888,1.0,0.9411764705882353
 native-country,Nicaragua,0.6666666666666666,1.0,0.8
-native-country,Vietnam,0.8333333333333334,1.0,0.9090909090909091
-native-country,Hong,0.8571428571428571,1.0,0.923076923076923
+native-country,Vietnam,1.0,1.0,1.0
+native-country,Hong,1.0,0.8333333333333334,0.9090909090909091
 native-country,Ireland,1.0,1.0,1.0
-native-country,Hungary,1.0,1.0,1.0
+native-country,Hungary,1.0,0.6666666666666666,0.8
 native-country,Holand-Netherlands,1.0,1.0,1.0

starter/starter/ml/model.py

@@ -22,7 +22,7 @@ def train_model(X_train, y_train):
         Trained machine learning model.
     """
     cv = StratifiedKFold(shuffle=True)
-    model = RandomForestClassifier(n_estimators=1000)
+    model = RandomForestClassifier()
     model.fit(X_train, y_train)
     scores = cross_val_score(model, X_train, y_train, scoring='f1',
                              cv=cv, n_jobs=-1)