Update dataset zip

dane.py

@@ -1,148 +1,148 @@
-### 1. Pobieranie zbioru danych
-import zipfile
-with zipfile.ZipFile("personal-key-indicators-of-heart-disease.zip", 'r') as zip_ref:
-    zip_ref.extractall("dataset_extracted")
-import pandas as pd
-# W pobranym zbiorze danych jest kilka podzbiorów więc celowo otwieram ten z NaN, żeby manualnie go oczyścić dla praktyki
-df = pd.read_csv("dataset_extracted/2022/heart_2022_with_nans.csv")
-## Przeglądanie nieoczyszczonego datasetu
-df.info()
-df.head()
-df.describe()
-df["HadHeartAttack"].value_counts().plot(kind="pie")
-df["HadHeartAttack"].value_counts()
-
-## 2. Podział na podzbiory (train / dev / test - 8:1:1)) i oversampling
-from sklearn.model_selection import train_test_split
-#Funkcji z sklearn musimy użyć dwukrotnie, bo dzieli tylko na dwa podzbiory
-train, test_and_valid = train_test_split(df, test_size=0.2) #0.8 train, 0.2 test&valid
-
-test, valid = train_test_split(test_and_valid, test_size=0.5) #0.1 test, 0.1 valid
-train["HadHeartAttack"].value_counts()
-def oversample(dataset):
-    num_true = len(dataset[dataset["HadHeartAttack"]=="Yes"])
-    num_false = len(dataset[dataset["HadHeartAttack"]=="No"])
-    num_oversampling_steps = num_false//num_true
-    oversampled = dataset.copy()
-    for x in range(num_oversampling_steps):
-        oversampled = pd.concat([oversampled, dataset[dataset["HadHeartAttack"]=="Yes"]], ignore_index=True)
-    return oversampled
-train = oversample(train)
-train["HadHeartAttack"].value_counts().plot(kind="pie")
-test["HadHeartAttack"].value_counts().plot(kind="pie")
-valid["HadHeartAttack"].value_counts().plot(kind="pie")
-df["SmokerStatus"].value_counts().plot(kind="pie")
-df["ECigaretteUsage"].value_counts().plot(kind="pie")
-df["CovidPos"].value_counts().plot(kind="pie")
-## Normalizacja część 1 - zamiana na kolumny liczbowe i kategoryczne
-df["Sex"].unique()
-df["GeneralHealth"].unique()
-health_map = {
-    "Excellent": 5,
-    "Very good": 4,
-    "Good": 3,
-    "Fair": 2,
-    "Poor": 1
-}
-for col in df:
-    print(f"{col}:")
-    print(df[col].unique())
-from collections import defaultdict
-def normalize_dataset(dataset):
-    dataset["GeneralHealth"] = dataset["GeneralHealth"].map(defaultdict(lambda: float('NaN'), health_map), na_action='ignore')
-    dataset["Sex"] = dataset["Sex"].map({"Female":0,"Male":1}).astype(float) #Zamiana z kolumn tekstowych na numeryczne
-    dataset.rename(columns ={"Sex":"Male"},inplace=True)
-    dataset["State"] = dataset["State"].astype('category')
-    dataset["PhysicalHealthDays"].astype(float)
-    dataset["MentalHealthDays"].astype(float)
-    dataset["LastCheckupTime"] = dataset["LastCheckupTime"].fillna("Unknown").astype('category') # Potem korzystam z fillna-->median ale nie działa to na kolumnach kategorycznych więc wykonuję to przed konwersją
-    dataset["PhysicalActivities"]= dataset["PhysicalActivities"].map({"No":0,"Yes":1})
-    dataset["SleepHours"].astype(float)
-    dataset["RemovedTeeth"] = dataset["RemovedTeeth"].map(defaultdict(lambda: float('NaN'), {"None of them":0,"1 to 5":1, "6 or more, but not all":2, "All":3}), na_action='ignore')
-    dataset["HadHeartAttack"]= dataset["HadHeartAttack"].map({"No":0,"Yes":1})
-    dataset["HadAngina"]= dataset["HadAngina"].map({"No":0,"Yes":1})
-    dataset["HadStroke"]= dataset["HadStroke"].map({"No":0,"Yes":1})
-    dataset["HadAsthma"]= dataset["HadAsthma"].map({"No":0,"Yes":1})
-    dataset["HadSkinCancer"]= dataset["HadSkinCancer"].map({"No":0,"Yes":1})
-    dataset["HadCOPD"]= dataset["HadCOPD"].map({"No":0,"Yes":1})
-    dataset["HadDepressiveDisorder"]= dataset["HadDepressiveDisorder"].map({"No":0,"Yes":1})
-    dataset["HadKidneyDisease"]= dataset["HadKidneyDisease"].map({"No":0,"Yes":1})
-    dataset["HadArthritis"]= dataset["HadArthritis"].map({"No":0,"Yes":1})
-    dataset["HadDiabetes"]= dataset["HadDiabetes"].map({"No":0,"Yes, but only during pregnancy (female)":1,"No, pre-diabetes or borderline diabetes":2,"Yes":3})
-
-    dataset["DeafOrHardOfHearing"]= dataset["DeafOrHardOfHearing"].map({"No":0,"Yes":1})
-    dataset["BlindOrVisionDifficulty"]= dataset["BlindOrVisionDifficulty"].map({"No":0,"Yes":1})
-    dataset["DifficultyConcentrating"]= dataset["DifficultyConcentrating"].map({"No":0,"Yes":1})
-    dataset["DifficultyWalking"]= dataset["DifficultyWalking"].map({"No":0,"Yes":1})
-    dataset["DifficultyDressingBathing"]= dataset["DifficultyDressingBathing"].map({"No":0,"Yes":1})
-    dataset["DifficultyErrands"]= dataset["DifficultyErrands"].map({"No":0,"Yes":1})
-    dataset["SmokerStatus"]= dataset["SmokerStatus"].map({"Never smoked":0,"Current smoker - now smokes some days":1,"Former smoker":2,"Current smoker - now smokes every day":3})
-    dataset["ECigaretteUsage"]= dataset["ECigaretteUsage"].map({"Never used e-cigarettes in my entire life":0,"Not at all (right now)":1,"Use them some days":2,"Use them every day":3})
-    dataset["ChestScan"]= dataset["ChestScan"].map({"No":0,"Yes":1})
-    dataset["RaceEthnicityCategory"] = dataset["RaceEthnicityCategory"].fillna("Unknown").astype('category')
-    dataset["AgeCategory"] = dataset["AgeCategory"].fillna("Unknown").astype('category')
-    dataset["HeightInMeters"] = dataset["HeightInMeters"].astype(float)
-    dataset["WeightInKilograms"] = dataset["WeightInKilograms"].astype(float)
-    dataset["BMI"] = dataset["BMI"].astype(float)
-    dataset["AlcoholDrinkers"]= dataset["AlcoholDrinkers"].map({"No":0,"Yes":1})
-    dataset["HIVTesting"]= dataset["HIVTesting"].map({"No":0,"Yes":1})
-    dataset["FluVaxLast12"]= dataset["FluVaxLast12"].map({"No":0,"Yes":1})
-    dataset["PneumoVaxEver"]= dataset["PneumoVaxEver"].map({"No":0,"Yes":1})
-    dataset["TetanusLast10Tdap"]= dataset["TetanusLast10Tdap"].apply(lambda x: float('NaN') if type(x)!=str else 1.0 if 'Yes,' in x else 1.0 if 'No,' in x else float('NaN'))
-    dataset["HighRiskLastYear"]= dataset["HighRiskLastYear"].map({"No":0,"Yes":1})
-    dataset["CovidPos"]= dataset["CovidPos"].map({"No":0,"Yes":1})
-test.head()
-normalize_dataset(test)
-test.head()
-test.info()
-normalize_dataset(train)
-normalize_dataset(valid)
-train.describe()
-test.describe()
-valid.describe()
-import seaborn as sns
-sns.set_theme()
-g = sns.catplot(
-    data=train, kind="bar",
-    x="GeneralHealth", y="WeightInKilograms", hue="HadHeartAttack",
-    errorbar="sd", palette="dark", alpha=.6, height=6
-)
-g.despine(left=True)
-g.set_axis_labels("General health index", "Body mass (kg)")
-g.legend.set_title("Had heart attack")
-valid.groupby('SmokerStatus', as_index=False)['HadHeartAttack'].mean()
-valid.groupby('GeneralHealth', as_index=False)['HadHeartAttack'].mean()
-valid.pivot_table('HadHeartAttack',index='GeneralHealth', columns='SmokerStatus')
-## Normalizacja część 2 - Skalowanie kolumn numerycznych do 0-1
-from sklearn.preprocessing import MinMaxScaler
-scaler = MinMaxScaler()
-def scale_float_columns(dataset):
-    numerical_columns = list(dataset.select_dtypes(include=['float64']).columns)
-    dataset[numerical_columns] = scaler.fit_transform(dataset[numerical_columns])
-test.head()
-scale_float_columns(test)
-scale_float_columns(train)
-scale_float_columns(valid)
-test.head()
-## 5. Czyszczenie brakujących pól
-print(df.shape[0])
-print(df.shape[0] - df.dropna().shape[0])
-test.head()
-
-numeric_columns = train.select_dtypes(include=['number']).columns
-test[numeric_columns] = test[numeric_columns].fillna(test[numeric_columns].median().iloc[0])
-train[numeric_columns] = train[numeric_columns].fillna(train[numeric_columns].median().iloc[0])
-valid[numeric_columns] = valid[numeric_columns].fillna(valid[numeric_columns].iloc[0])
-
-test.head()
-test["HighRiskLastYear"].value_counts()
-test["HighRiskLastYear"].isna().sum()
-test.info()
-train.info()
-valid.info()
-
-cat_columns = test.select_dtypes(['category']).columns
-
-test.to_csv("test.csv")
-train.to_csv("train.csv")
+### 1. Pobieranie zbioru danych
+import zipfile
+with zipfile.ZipFile("personal-key-indicators-of-heart-disease.zip", 'r') as zip_ref:
+    zip_ref.extractall("dataset_extracted")
+import pandas as pd
+# W pobranym zbiorze danych jest kilka podzbiorów więc celowo otwieram ten z NaN, żeby manualnie go oczyścić dla praktyki
+df = pd.read_csv("dataset_extracted/2022/heart_2022_with_nans.csv")
+## Przeglądanie nieoczyszczonego datasetu
+df.info()
+df.head()
+df.describe()
+df["HadHeartAttack"].value_counts().plot(kind="pie")
+df["HadHeartAttack"].value_counts()
+
+## 2. Podział na podzbiory (train / dev / test - 8:1:1)) i oversampling
+from sklearn.model_selection import train_test_split
+#Funkcji z sklearn musimy użyć dwukrotnie, bo dzieli tylko na dwa podzbiory
+train, test_and_valid = train_test_split(df, test_size=0.2) #0.8 train, 0.2 test&valid
+
+test, valid = train_test_split(test_and_valid, test_size=0.5) #0.1 test, 0.1 valid
+train["HadHeartAttack"].value_counts()
+def oversample(dataset):
+    num_true = len(dataset[dataset["HadHeartAttack"]=="Yes"])
+    num_false = len(dataset[dataset["HadHeartAttack"]=="No"])
+    num_oversampling_steps = num_false//num_true
+    oversampled = dataset.copy()
+    for x in range(num_oversampling_steps):
+        oversampled = pd.concat([oversampled, dataset[dataset["HadHeartAttack"]=="Yes"]], ignore_index=True)
+    return oversampled
+train = oversample(train)
+train["HadHeartAttack"].value_counts().plot(kind="pie")
+test["HadHeartAttack"].value_counts().plot(kind="pie")
+valid["HadHeartAttack"].value_counts().plot(kind="pie")
+df["SmokerStatus"].value_counts().plot(kind="pie")
+df["ECigaretteUsage"].value_counts().plot(kind="pie")
+df["CovidPos"].value_counts().plot(kind="pie")
+## Normalizacja część 1 - zamiana na kolumny liczbowe i kategoryczne
+df["Sex"].unique()
+df["GeneralHealth"].unique()
+health_map = {
+    "Excellent": 5,
+    "Very good": 4,
+    "Good": 3,
+    "Fair": 2,
+    "Poor": 1
+}
+for col in df:
+    print(f"{col}:")
+    print(df[col].unique())
+from collections import defaultdict
+def normalize_dataset(dataset):
+    dataset["GeneralHealth"] = dataset["GeneralHealth"].map(defaultdict(lambda: float('NaN'), health_map), na_action='ignore')
+    dataset["Sex"] = dataset["Sex"].map({"Female":0,"Male":1}).astype(float) #Zamiana z kolumn tekstowych na numeryczne
+    dataset.rename(columns ={"Sex":"Male"},inplace=True)
+    dataset["State"] = dataset["State"].astype('category')
+    dataset["PhysicalHealthDays"].astype(float)
+    dataset["MentalHealthDays"].astype(float)
+    dataset["LastCheckupTime"] = dataset["LastCheckupTime"].fillna("Unknown").astype('category') # Potem korzystam z fillna-->median ale nie działa to na kolumnach kategorycznych więc wykonuję to przed konwersją
+    dataset["PhysicalActivities"]= dataset["PhysicalActivities"].map({"No":0,"Yes":1})
+    dataset["SleepHours"].astype(float)
+    dataset["RemovedTeeth"] = dataset["RemovedTeeth"].map(defaultdict(lambda: float('NaN'), {"None of them":0,"1 to 5":1, "6 or more, but not all":2, "All":3}), na_action='ignore')
+    dataset["HadHeartAttack"]= dataset["HadHeartAttack"].map({"No":0,"Yes":1})
+    dataset["HadAngina"]= dataset["HadAngina"].map({"No":0,"Yes":1})
+    dataset["HadStroke"]= dataset["HadStroke"].map({"No":0,"Yes":1})
+    dataset["HadAsthma"]= dataset["HadAsthma"].map({"No":0,"Yes":1})
+    dataset["HadSkinCancer"]= dataset["HadSkinCancer"].map({"No":0,"Yes":1})
+    dataset["HadCOPD"]= dataset["HadCOPD"].map({"No":0,"Yes":1})
+    dataset["HadDepressiveDisorder"]= dataset["HadDepressiveDisorder"].map({"No":0,"Yes":1})
+    dataset["HadKidneyDisease"]= dataset["HadKidneyDisease"].map({"No":0,"Yes":1})
+    dataset["HadArthritis"]= dataset["HadArthritis"].map({"No":0,"Yes":1})
+    dataset["HadDiabetes"]= dataset["HadDiabetes"].map({"No":0,"Yes, but only during pregnancy (female)":1,"No, pre-diabetes or borderline diabetes":2,"Yes":3})
+
+    dataset["DeafOrHardOfHearing"]= dataset["DeafOrHardOfHearing"].map({"No":0,"Yes":1})
+    dataset["BlindOrVisionDifficulty"]= dataset["BlindOrVisionDifficulty"].map({"No":0,"Yes":1})
+    dataset["DifficultyConcentrating"]= dataset["DifficultyConcentrating"].map({"No":0,"Yes":1})
+    dataset["DifficultyWalking"]= dataset["DifficultyWalking"].map({"No":0,"Yes":1})
+    dataset["DifficultyDressingBathing"]= dataset["DifficultyDressingBathing"].map({"No":0,"Yes":1})
+    dataset["DifficultyErrands"]= dataset["DifficultyErrands"].map({"No":0,"Yes":1})
+    dataset["SmokerStatus"]= dataset["SmokerStatus"].map({"Never smoked":0,"Current smoker - now smokes some days":1,"Former smoker":2,"Current smoker - now smokes every day":3})
+    dataset["ECigaretteUsage"]= dataset["ECigaretteUsage"].map({"Never used e-cigarettes in my entire life":0,"Not at all (right now)":1,"Use them some days":2,"Use them every day":3})
+    dataset["ChestScan"]= dataset["ChestScan"].map({"No":0,"Yes":1})
+    dataset["RaceEthnicityCategory"] = dataset["RaceEthnicityCategory"].fillna("Unknown").astype('category')
+    dataset["AgeCategory"] = dataset["AgeCategory"].fillna("Unknown").astype('category')
+    dataset["HeightInMeters"] = dataset["HeightInMeters"].astype(float)
+    dataset["WeightInKilograms"] = dataset["WeightInKilograms"].astype(float)
+    dataset["BMI"] = dataset["BMI"].astype(float)
+    dataset["AlcoholDrinkers"]= dataset["AlcoholDrinkers"].map({"No":0,"Yes":1})
+    dataset["HIVTesting"]= dataset["HIVTesting"].map({"No":0,"Yes":1})
+    dataset["FluVaxLast12"]= dataset["FluVaxLast12"].map({"No":0,"Yes":1})
+    dataset["PneumoVaxEver"]= dataset["PneumoVaxEver"].map({"No":0,"Yes":1})
+    dataset["TetanusLast10Tdap"]= dataset["TetanusLast10Tdap"].apply(lambda x: float('NaN') if type(x)!=str else 1.0 if 'Yes,' in x else 1.0 if 'No,' in x else float('NaN'))
+    dataset["HighRiskLastYear"]= dataset["HighRiskLastYear"].map({"No":0,"Yes":1})
+    dataset["CovidPos"]= dataset["CovidPos"].map({"No":0,"Yes":1})
+test.head()
+normalize_dataset(test)
+test.head()
+test.info()
+normalize_dataset(train)
+normalize_dataset(valid)
+train.describe()
+test.describe()
+valid.describe()
+import seaborn as sns
+sns.set_theme()
+g = sns.catplot(
+    data=train, kind="bar",
+    x="GeneralHealth", y="WeightInKilograms", hue="HadHeartAttack",
+    errorbar="sd", palette="dark", alpha=.6, height=6
+)
+g.despine(left=True)
+g.set_axis_labels("General health index", "Body mass (kg)")
+g.legend.set_title("Had heart attack")
+valid.groupby('SmokerStatus', as_index=False)['HadHeartAttack'].mean()
+valid.groupby('GeneralHealth', as_index=False)['HadHeartAttack'].mean()
+valid.pivot_table('HadHeartAttack',index='GeneralHealth', columns='SmokerStatus')
+## Normalizacja część 2 - Skalowanie kolumn numerycznych do 0-1
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler()
+def scale_float_columns(dataset):
+    numerical_columns = list(dataset.select_dtypes(include=['float64']).columns)
+    dataset[numerical_columns] = scaler.fit_transform(dataset[numerical_columns])
+test.head()
+scale_float_columns(test)
+scale_float_columns(train)
+scale_float_columns(valid)
+test.head()
+## 5. Czyszczenie brakujących pól
+print(df.shape[0])
+print(df.shape[0] - df.dropna().shape[0])
+test.head()
+
+numeric_columns = train.select_dtypes(include=['number']).columns
+test[numeric_columns] = test[numeric_columns].fillna(test[numeric_columns].median().iloc[0])
+train[numeric_columns] = train[numeric_columns].fillna(train[numeric_columns].median().iloc[0])
+valid[numeric_columns] = valid[numeric_columns].fillna(valid[numeric_columns].median().iloc[0])
+
+test.head()
+test["HighRiskLastYear"].value_counts()
+test["HighRiskLastYear"].isna().sum()
+test.info()
+train.info()
+valid.info()
+
+cat_columns = test.select_dtypes(['category']).columns
+
+test.to_csv("test.csv")
+train.to_csv("train.csv")
 valid.to_csv("valid.csv")

environment.yml

@@ -0,0 +1,236 @@
+name: ML
+channels:
+  - defaults
+  - conda-forge
+dependencies:
+  - _tflow_select=2.3.0=mkl
+  - absl-py=2.1.0=py39haa95532_0
+  - aiohttp=3.9.5=py39h2bbff1b_0
+  - aiosignal=1.2.0=pyhd3eb1b0_0
+  - anyio=4.2.0=py39haa95532_0
+  - argon2-cffi=21.3.0=pyhd3eb1b0_0
+  - argon2-cffi-bindings=21.2.0=py39h2bbff1b_0
+  - asttokens=2.0.5=pyhd3eb1b0_0
+  - astunparse=1.6.3=py_0
+  - async-lru=2.0.4=py39haa95532_0
+  - async-timeout=4.0.3=py39haa95532_0
+  - attrs=23.1.0=py39haa95532_0
+  - babel=2.11.0=py39haa95532_0
+  - backcall=0.2.0=pyhd3eb1b0_0
+  - beautifulsoup4=4.12.2=py39haa95532_0
+  - blas=1.0=mkl
+  - bleach=4.1.0=pyhd3eb1b0_0
+  - blinker=1.6.2=py39haa95532_0
+  - bottleneck=1.3.7=py39h9128911_0
+  - brotli=1.0.9=h2bbff1b_8
+  - brotli-bin=1.0.9=h2bbff1b_8
+  - brotli-python=1.0.9=py39hd77b12b_8
+  - bzip2=1.0.8=h2bbff1b_6
+  - ca-certificates=2024.3.11=haa95532_0
+  - cachetools=5.3.3=py39haa95532_0
+  - certifi=2024.2.2=py39haa95532_0
+  - cffi=1.16.0=py39h2bbff1b_1
+  - charset-normalizer=2.0.4=pyhd3eb1b0_0
+  - click=8.1.7=py39haa95532_0
+  - colorama=0.4.6=py39haa95532_0
+  - comm=0.2.1=py39haa95532_0
+  - contourpy=1.2.0=py39h59b6b97_0
+  - cryptography=41.0.3=py39h3438e0d_0
+  - cycler=0.11.0=pyhd3eb1b0_0
+  - debugpy=1.6.7=py39hd77b12b_0
+  - decorator=5.1.1=pyhd3eb1b0_0
+  - defusedxml=0.7.1=pyhd3eb1b0_0
+  - exceptiongroup=1.2.0=py39haa95532_0
+  - executing=0.8.3=pyhd3eb1b0_0
+  - flatbuffers=2.0.0=h6c2663c_0
+  - fonttools=4.51.0=py39h2bbff1b_0
+  - freetype=2.12.1=ha860e81_0
+  - frozenlist=1.4.0=py39h2bbff1b_0
+  - gast=0.4.0=pyhd3eb1b0_0
+  - giflib=5.2.1=h8cc25b3_3
+  - glib=2.78.4=hd77b12b_0
+  - glib-tools=2.78.4=hd77b12b_0
+  - google-auth=2.29.0=py39haa95532_0
+  - google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
+  - google-pasta=0.2.0=pyhd3eb1b0_0
+  - grpcio=1.42.0=py39hc60d5dd_0
+  - gst-plugins-base=1.18.5=h9e645db_0
+  - gstreamer=1.18.5=hd78058f_0
+  - h5py=3.11.0=py39hed405ee_0
+  - hdf5=1.12.1=h51c971a_3
+  - icc_rt=2022.1.0=h6049295_2
+  - icu=58.2=ha925a31_3
+  - idna=3.7=py39haa95532_0
+  - importlib-metadata=7.0.1=py39haa95532_0
+  - importlib_metadata=7.0.1=hd3eb1b0_0
+  - importlib_resources=6.1.1=py39haa95532_1
+  - intel-openmp=2023.1.0=h59b6b97_46320
+  - ipykernel=6.28.0=py39haa95532_0
+  - ipython=8.15.0=py39haa95532_0
+  - ipywidgets=8.1.2=py39haa95532_0
+  - jedi=0.18.1=py39haa95532_1
+  - jinja2=3.1.3=py39haa95532_0
+  - joblib=1.4.0=py39haa95532_0
+  - jpeg=9e=h2bbff1b_1
+  - json5=0.9.6=pyhd3eb1b0_0
+  - jsonschema=4.19.2=py39haa95532_0
+  - jsonschema-specifications=2023.7.1=py39haa95532_0
+  - jupyter=1.0.0=py39haa95532_9
+  - jupyter-lsp=2.2.0=py39haa95532_0
+  - jupyter_client=8.6.0=py39haa95532_0
+  - jupyter_console=6.6.3=py39haa95532_0
+  - jupyter_core=5.5.0=py39haa95532_0
+  - jupyter_events=0.8.0=py39haa95532_0
+  - jupyter_server=2.10.0=py39haa95532_0
+  - jupyter_server_terminals=0.4.4=py39haa95532_1
+  - jupyterlab=4.0.11=py39haa95532_0
+  - jupyterlab_pygments=0.1.2=py_0
+  - jupyterlab_server=2.25.1=py39haa95532_0
+  - jupyterlab_widgets=3.0.10=py39haa95532_0
+  - keras=2.10.0=py39haa95532_0
+  - keras-preprocessing=1.1.2=pyhd3eb1b0_0
+  - kiwisolver=1.4.4=py39hd77b12b_0
+  - krb5=1.19.4=h5b6d351_0
+  - lcms2=2.12=h83e58a3_0
+  - lerc=3.0=hd77b12b_0
+  - libbrotlicommon=1.0.9=h2bbff1b_8
+  - libbrotlidec=1.0.9=h2bbff1b_8
+  - libbrotlienc=1.0.9=h2bbff1b_8
+  - libclang=14.0.6=default_hb5a9fac_1
+  - libclang13=14.0.6=default_h8e68704_1
+  - libcurl=8.7.1=h86230a5_0
+  - libdeflate=1.17=h2bbff1b_1
+  - libffi=3.4.4=hd77b12b_1
+  - libglib=2.78.4=ha17d25a_0
+  - libiconv=1.16=h2bbff1b_3
+  - libogg=1.3.5=h2bbff1b_1
+  - libpng=1.6.39=h8cc25b3_0
+  - libprotobuf=3.20.3=h23ce68f_0
+  - libsodium=1.0.18=h62dcd97_0
+  - libssh2=1.10.0=hcd4344a_2
+  - libtiff=4.5.1=hd77b12b_0
+  - libvorbis=1.3.7=he774522_0
+  - libwebp-base=1.3.2=h2bbff1b_0
+  - lz4-c=1.9.4=h2bbff1b_1
+  - markdown=3.4.1=py39haa95532_0
+  - markupsafe=2.1.3=py39h2bbff1b_0
+  - matplotlib-base=3.8.4=py39h4ed8f06_0
+  - matplotlib-inline=0.1.6=py39haa95532_0
+  - mistune=2.0.4=py39haa95532_0
+  - mkl=2023.1.0=h6b88ed4_46358
+  - mkl-service=2.4.0=py39h2bbff1b_1
+  - mkl_fft=1.3.8=py39h2bbff1b_0
+  - mkl_random=1.2.4=py39h59b6b97_0
+  - multidict=6.0.4=py39h2bbff1b_0
+  - nbclient=0.8.0=py39haa95532_0
+  - nbconvert=7.10.0=py39haa95532_0
+  - nbformat=5.9.2=py39haa95532_0
+  - nest-asyncio=1.6.0=py39haa95532_0
+  - notebook=7.0.8=py39haa95532_0
+  - notebook-shim=0.2.3=py39haa95532_0
+  - numexpr=2.8.7=py39h2cd9be0_0
+  - numpy=1.26.4=py39h055cbcc_0
+  - numpy-base=1.26.4=py39h65a83cf_0
+  - oauthlib=3.2.2=py39haa95532_0
+  - openjpeg=2.4.0=h4fc8c34_0
+  - openssl=1.1.1w=h2bbff1b_0
+  - opt_einsum=3.3.0=pyhd3eb1b0_1
+  - overrides=7.4.0=py39haa95532_0
+  - packaging=23.2=py39haa95532_0
+  - pandas=2.2.1=py39h5da7b33_0
+  - pandocfilters=1.5.0=pyhd3eb1b0_0
+  - parso=0.8.3=pyhd3eb1b0_0
+  - pcre2=10.42=h0ff8eda_1
+  - pickleshare=0.7.5=pyhd3eb1b0_1003
+  - pillow=10.3.0=py39h2bbff1b_0
+  - pip=24.0=py39haa95532_0
+  - platformdirs=3.10.0=py39haa95532_0
+  - ply=3.11=py39haa95532_0
+  - prometheus_client=0.14.1=py39haa95532_0
+  - prompt-toolkit=3.0.43=py39haa95532_0
+  - prompt_toolkit=3.0.43=hd3eb1b0_0
+  - protobuf=3.20.3=py39hd77b12b_0
+  - psutil=5.9.0=py39h2bbff1b_0
+  - pure_eval=0.2.2=pyhd3eb1b0_0
+  - pyasn1=0.4.8=pyhd3eb1b0_0
+  - pyasn1-modules=0.2.8=py_0
+  - pybind11-abi=5=hd3eb1b0_0
+  - pycparser=2.21=pyhd3eb1b0_0
+  - pygments=2.15.1=py39haa95532_1
+  - pyjwt=2.8.0=py39haa95532_0
+  - pyopenssl=23.2.0=py39haa95532_0
+  - pyparsing=3.0.9=py39haa95532_0
+  - pyqt=5.15.10=py39hd77b12b_0
+  - pyqt5-sip=12.13.0=py39h2bbff1b_0
+  - pysocks=1.7.1=py39haa95532_0
+  - python=3.9.18=h6244533_0
+  - python-dateutil=2.9.0post0=py39haa95532_0
+  - python-fastjsonschema=2.16.2=py39haa95532_0
+  - python-flatbuffers=2.0=pyhd3eb1b0_0
+  - python-json-logger=2.0.7=py39haa95532_0
+  - python-tzdata=2023.3=pyhd3eb1b0_0
+  - pytz=2024.1=py39haa95532_0
+  - pywin32=305=py39h2bbff1b_0
+  - pywinpty=2.0.10=py39h5da7b33_0
+  - pyyaml=6.0.1=py39h2bbff1b_0
+  - pyzmq=25.1.2=py39hd77b12b_0
+  - qt-main=5.15.2=he8e5bd7_8
+  - qtconsole=5.5.1=py39haa95532_0
+  - qtpy=2.4.1=py39haa95532_0
+  - referencing=0.30.2=py39haa95532_0
+  - requests=2.31.0=py39haa95532_1
+  - requests-oauthlib=1.3.0=py_0
+  - rfc3339-validator=0.1.4=py39haa95532_0
+  - rfc3986-validator=0.1.1=py39haa95532_0
+  - rpds-py=0.10.6=py39h062c2fa_0
+  - rsa=4.7.2=pyhd3eb1b0_1
+  - scikit-learn=1.4.2=py39h4ed8f06_1
+  - scipy=1.13.0=py39h8640f81_0
+  - seaborn=0.12.2=py39haa95532_0
+  - send2trash=1.8.2=py39haa95532_0
+  - setuptools=69.5.1=py39haa95532_0
+  - sip=6.7.12=py39hd77b12b_0
+  - six=1.16.0=pyhd3eb1b0_1
+  - snappy=1.1.10=h6c2663c_1
+  - sniffio=1.3.0=py39haa95532_0
+  - soupsieve=2.5=py39haa95532_0
+  - sqlite=3.45.3=h2bbff1b_0
+  - stack_data=0.2.0=pyhd3eb1b0_0
+  - tbb=2021.8.0=h59b6b97_0
+  - tensorboard=2.10.0=py39haa95532_0
+  - tensorboard-data-server=0.6.1=py39haa95532_0
+  - tensorboard-plugin-wit=1.8.1=py39haa95532_0
+  - tensorflow=2.10.0=mkl_py39ha510bab_0
+  - tensorflow-base=2.10.0=mkl_py39h6a7f48e_0
+  - tensorflow-estimator=2.10.0=py39haa95532_0
+  - termcolor=2.1.0=py39haa95532_0
+  - terminado=0.17.1=py39haa95532_0
+  - threadpoolctl=2.2.0=pyh0d69192_0
+  - tinycss2=1.2.1=py39haa95532_0
+  - tomli=2.0.1=py39haa95532_0
+  - tornado=6.3.3=py39h2bbff1b_0
+  - traitlets=5.7.1=py39haa95532_0
+  - typing-extensions=4.11.0=py39haa95532_0
+  - typing_extensions=4.11.0=py39haa95532_0
+  - tzdata=2024a=h04d1e81_0
+  - unicodedata2=15.1.0=py39h2bbff1b_0
+  - urllib3=2.2.1=py39haa95532_0
+  - vc=14.2=h2eaa2aa_1
+  - vs2015_runtime=14.29.30133=h43f2093_3
+  - wcwidth=0.2.5=pyhd3eb1b0_0
+  - webencodings=0.5.1=py39haa95532_1
+  - websocket-client=1.8.0=py39haa95532_0
+  - werkzeug=2.3.8=py39haa95532_0
+  - wheel=0.43.0=py39haa95532_0
+  - widgetsnbextension=4.0.10=py39haa95532_0
+  - win_inet_pton=1.1.0=py39haa95532_0
+  - winpty=0.4.3=4
+  - wrapt=1.14.1=py39h2bbff1b_0
+  - xz=5.4.6=h8cc25b3_1
+  - yaml=0.2.5=he774522_0
+  - yarl=1.9.3=py39h2bbff1b_0
+  - zeromq=4.3.5=hd77b12b_0
+  - zipp=3.17.0=py39haa95532_0
+  - zlib=1.2.13=h8cc25b3_1
+  - zstd=1.5.5=hd43e919_2
+prefix: C:\Users\Adrian\miniconda3\envs\ML

evaluate.py

@@ -0,0 +1,81 @@
+import pandas as pd
+valid = pd.read_csv("valid.csv")
+
+x_columns = ['Male', 'GeneralHealth', 'PhysicalHealthDays', 'MentalHealthDays',
+       'PhysicalActivities', 'SleepHours', 'RemovedTeeth',
+       'HadAngina', 'HadStroke', 'HadAsthma', 'HadSkinCancer', 'HadCOPD',
+       'HadDepressiveDisorder', 'HadKidneyDisease', 'HadArthritis',
+       'HadDiabetes', 'DeafOrHardOfHearing', 'BlindOrVisionDifficulty',
+       'DifficultyConcentrating', 'DifficultyWalking',
+       'DifficultyDressingBathing', 'DifficultyErrands', 'SmokerStatus',
+       'ECigaretteUsage', 'ChestScan', 'HeightInMeters', 'WeightInKilograms',
+       'BMI', 'AlcoholDrinkers', 'HIVTesting', 'FluVaxLast12', 'PneumoVaxEver',
+       'TetanusLast10Tdap', 'HighRiskLastYear', 'CovidPos']
+y_column = 'HadHeartAttack'
+
+valid_x = valid[x_columns]
+valid_y = valid[y_column]
+
+from tensorflow import keras
+model = keras.models.load_model('model.keras')
+
+import numpy as np
+predictions = model.predict(valid_x)[:,0]
+true_answers = valid_y.to_numpy()
+validation_accuracy = np.sum(np.rint(predictions) == true_answers)/len(true_answers)
+print(f"Poprawność na zbiorze walidacyjnym: {validation_accuracy:.2%}")
+
+np.savetxt("predictions.txt",predictions)
+np.savetxt("predictions_two_digits.txt",predictions, fmt='%1.2f')
+
+validate_heart_disease_true = valid.loc[valid[y_column]==1]
+validate_heart_disease_false = valid.loc[valid[y_column]==0]
+
+from datetime import timezone
+import datetime
+import json
+
+validate_heart_disease_true_x = validate_heart_disease_true[x_columns]
+validate_heart_disease_false_x = validate_heart_disease_false[x_columns]
+
+
+
+predictions_for_true = model.predict(validate_heart_disease_true_x)[:,0]
+predictions_for_false = model.predict(validate_heart_disease_false_x)[:,0]
+
+true_positives = np.sum(np.rint(predictions_for_true) == np.ones_like(predictions_for_true)).tolist()
+true_negatives = np.sum(np.rint(predictions_for_false) == np.zeros_like(predictions_for_false)).tolist()
+false_positives = len(predictions_for_false)-true_negatives
+false_negatives = len(predictions_for_true)-true_positives
+
+
+current_datetime = datetime.datetime.now(timezone.utc)
+metrics = {"true_positives": true_positives, "true_negatives": true_negatives, "false_positives": false_positives, "false_negatives" : false_negatives, "datetime_utc" : str(current_datetime)}
+history = []
+try:
+    with open("metrics.json","r") as f:
+       history = json.load(f)
+except FileNotFoundError:
+    print('No historical metrics found')
+
+history.append(metrics)
+with open("metrics.json","w") as f:
+       json.dump(history, f)
+
+
+import matplotlib.pyplot as plt
+true_positives_history = [x["true_positives"] for x in history]
+true_negatives_history = [x["true_negatives"] for x in history]
+false_positives_history = [x["false_positives"] for x in history]
+false_negatives_history = [x["false_negatives"] for x in history]
+
+plt.plot(true_positives_history)
+plt.plot(true_negatives_history)
+plt.plot(false_positives_history)
+plt.plot(false_negatives_history)
+
+plt.legend(["True positives", "True negatives", "False positives", "False negatives"])
+plt.xlabel("Build number")
+plt.ylabel("Metric value")
+plt.title("Model evaluation history")
+plt.savefig("metrics.jpg")