zad7.1

Zad7/Zajęcia7/my_runs/1/config.json

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+{
+  "seed": 770691781,
+  "test_size_param": 0.2,
+  "train_size_param": 0.8
+}

Zad7/Zajęcia7/my_runs/1/info.json

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+{
+  "prepare_model_ts": "2021-05-10 01:14:47.306858"
+}

Zad7/Zajęcia7/my_runs/1/metrics.json

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+{}

Zad7/Zajęcia7/my_runs/1/run.json

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+{
+  "artifacts": [],
+  "command": "my_main",
+  "experiment": {
+    "base_dir": "/home/tomasz/IUM/ium_434695/Zad7",
+    "dependencies": [
+      "numpy==1.19.5",
+      "pandas==1.2.3",
+      "requests==2.22.0",
+      "sacred==0.8.2",
+      "scikit-learn==0.24.1",
+      "tensorflow==2.4.1",
+      "wget==3.2"
+    ],
+    "mainfile": "zadanie1.py",
+    "name": "file_observer",
+    "repositories": [
+      {
+        "commit": "2f71b4340e1a7092c2640a0446109758bcd02dad",
+        "dirty": false,
+        "url": "https://git.wmi.amu.edu.pl/s434695/ium_434695.git"
+      }
+    ],
+    "sources": [
+      [
+        "zadanie1.py",
+        "_sources/zadanie1_4b3c4820a644952451146e0b432e8947.py"
+      ]
+    ]
+  },
+  "heartbeat": "2021-05-09T23:30:03.895239",
+  "host": {
+    "ENV": {},
+    "cpu": "Intel(R) Core(TM) i7-3520M CPU @ 2.90GHz",
+    "hostname": "tomasz-HP-EliteBook-8570p",
+    "os": [
+      "Linux",
+      "Linux-5.8.0-50-generic-x86_64-with-glibc2.29"
+    ],
+    "python_version": "3.8.5"
+  },
+  "meta": {
+    "command": "my_main",
+    "options": {
+      "--beat-interval": null,
+      "--capture": null,
+      "--comment": null,
+      "--debug": false,
+      "--enforce_clean": false,
+      "--file_storage": null,
+      "--force": false,
+      "--help": false,
+      "--loglevel": null,
+      "--mongo_db": null,
+      "--name": null,
+      "--pdb": false,
+      "--print-config": false,
+      "--priority": null,
+      "--queue": false,
+      "--s3": null,
+      "--sql": null,
+      "--tiny_db": null,
+      "--unobserved": false
+    }
+  },
+  "resources": [],
+  "result": null,
+  "start_time": "2021-05-09T23:14:47.279713",
+  "status": "COMPLETED",
+  "stop_time": "2021-05-09T23:30:03.883313"
+}

Zad7/Zajęcia7/my_runs/_sources/zadanie1_4b3c4820a644952451146e0b432e8947.py

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+#! /usr/bin/python3
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense
+from sklearn.metrics import accuracy_score, classification_report
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import wget
+import numpy as np
+import requests
+from sacred.observers import FileStorageObserver
+from sacred import Experiment
+from datetime import datetime
+import os
+
+ex = Experiment("file_observer", interactive=True)
+
+ex.observers.append(FileStorageObserver('Zajęcia7/my_runs'))
+
+@ex.config
+def my_config():
+    train_size_param = 0.8
+    test_size_param = 0.2
+
+@ex.capture
+def prepare_model(train_size_param, test_size_param, _run):
+    _run.info["prepare_model_ts"] = str(datetime.now())
+
+    url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
+    r = requests.get(url, allow_redirects=True)
+
+    open('vgsales.csv', 'wb').write(r.content)
+    df = pd.read_csv('vgsales.csv')
+
+
+
+    def regression_model():
+        model = Sequential()
+        model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
+        model.add(Dense(64,activation = "relu"))
+        model.add(Dense(1,activation = "relu"))
+        
+        model.compile(optimizer = "adam", loss = "mean_squared_error")
+        return model
+
+    df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
+    df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
+    df
+
+    y = df.Nintendo
+
+    df=((df-df.min())/(df.max()-df.min()))
+
+    x = df.drop(['Nintendo'],axis = 1)
+
+    x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
+
+    model = regression_model()
+    model.fit(x_train, y_train, epochs = 600, verbose = 1)
+
+    y_pred = model.predict(x_test)
+
+    y_pred[:5]
+
+    y_pred = np.around(y_pred, decimals=0)
+
+    y_pred[:5]
+
+    return(classification_report(y_test,y_pred))
+
+@ex.main
+def my_main(train_size_param, test_size_param):
+    print(prepare_model()) ## Nie musimy przekazywać wartości
+
+
+r = ex.run()
+ex.add_artifact("Zajęcia7/saved_model/saved_model.pb")

Zad7/zadanie1.py

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+#! /usr/bin/python3
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense
+from sklearn.metrics import accuracy_score, classification_report
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import wget
+import numpy as np
+import requests
+from sacred.observers import FileStorageObserver
+from sacred import Experiment
+from datetime import datetime
+import os
+
+ex = Experiment("file_observer", interactive=True)
+
+ex.observers.append(FileStorageObserver('Zajęcia7/my_runs'))
+
+@ex.config
+def my_config():
+    train_size_param = 0.8
+    test_size_param = 0.2
+
+@ex.capture
+def prepare_model(train_size_param, test_size_param, _run):
+    _run.info["prepare_model_ts"] = str(datetime.now())
+
+    url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
+    r = requests.get(url, allow_redirects=True)
+
+    open('vgsales.csv', 'wb').write(r.content)
+    df = pd.read_csv('vgsales.csv')
+
+
+
+    def regression_model():
+        model = Sequential()
+        model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
+        model.add(Dense(64,activation = "relu"))
+        model.add(Dense(1,activation = "relu"))
+        
+        model.compile(optimizer = "adam", loss = "mean_squared_error")
+        return model
+
+    df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
+    df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
+    df
+
+    y = df.Nintendo
+
+    df=((df-df.min())/(df.max()-df.min()))
+
+    x = df.drop(['Nintendo'],axis = 1)
+
+    x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
+
+    model = regression_model()
+    model.fit(x_train, y_train, epochs = 600, verbose = 1)
+
+    y_pred = model.predict(x_test)
+
+    y_pred[:5]
+
+    y_pred = np.around(y_pred, decimals=0)
+
+    y_pred[:5]
+
+    return(classification_report(y_test,y_pred))
+
+@ex.main
+def my_main(train_size_param, test_size_param):
+    print(prepare_model()) ## Nie musimy przekazywać wartości
+
+
+r = ex.run()
+ex.add_artifact("Zajęcia7/saved_model/saved_model.pb")