linting

mikamyrseth · Oct 6, 2022 · 1f6af75 · 1f6af75
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Showing 3 changed files with 51 additions and 49 deletions.
diff --git a/input/custom_dataseries/ellen_custom.json b/input/custom_dataseries/ellen_custom.json
@@ -1,10 +1,11 @@
 [
-    {
-        "name": "CUSTOM-ELLEN-INTEREST-DIFFERENCE",
-        "page": "3",
-        "weights": {
-            "NO-MONEY-MARKET-12": 1,
-            "FOREIGN-MONEY-MARKET-12": -1
-        }
+  {
+    "name": "CUSTOM-ELLEN-INTEREST-DIFFERENCE",
+    "page": "3",
+    "type": "ADD",
+    "weights": {
+      "NO-MONEY-MARKET-12": 1,
+      "FOREIGN-MONEY-MARKET-12": -1
     }
-]
+  }
+]
diff --git a/input/models/ellen_asymmetric.json b/input/models/ellen_asymmetric.json
@@ -1,30 +1,29 @@
 {
-    "name": "Ellen asymmetric",
-    "authors": "Saskia Ter Ellen",
-    "publish_year": 2016,
-    "page": "5",
-    "model_start_date": "2001-01-01",
-    "model_end_date": "2015-08-01",
-    "dependent_variable": "DELTA-LOG-NB-I44", 
-    "frequency": "MONTHLY",
-    "coeffs": {
-      "ALPHA": 0.0018,
-      "DELTA_LOG_ICE_BRENT_POSITIVE": -0.0776,
-      "DELTA_LOG_ICE_BRENT_NEGATIVE": -0.0562,
-      "LAGGED_INTEREST_DIFFERENTIAL": -0.0015,
-      "CHANGE_INTEREST_DIFFERENTIAL": -0.0275,
-      "CRISIS_CONTROL_DUMMY": -0.0219
-    },
-    "stds": {
-        "CONSTANT": 1.3992,
-        "DELTA_LOG_ICE_BRENT_POSITIVE": -3.1713,
-        "DELTA_LOG_ICE_BRENT_NEGATIVE": -2.2304,
-        "LAGGED_INTEREST_DIFFERENTIAL": -1.9094,
-        "CHANGE_INTEREST_DIFFERENTIAL": -6.3554,
-        "CRISIS_CONTROL_DUMMY": -0.0219
-    },
-    "stats": {
-      "adjustedR2":0.3339
-    }
+  "name": "Ellen asymmetric",
+  "authors": "Saskia Ter Ellen",
+  "publish_year": 2016,
+  "page": "5",
+  "model_start_date": "2001-01-01",
+  "model_end_date": "2015-08-01",
+  "dependent_variable": "DELTA-LOG-NB-I44",
+  "frequency": "MONTHLY",
+  "coeffs": {
+    "ALPHA": 0.0018,
+    "DELTA_LOG_ICE_BRENT_POSITIVE": -0.0776,
+    "DELTA_LOG_ICE_BRENT_NEGATIVE": -0.0562,
+    "LAGGED_INTEREST_DIFFERENTIAL": -0.0015,
+    "CHANGE_INTEREST_DIFFERENTIAL": -0.0275,
+    "CRISIS_CONTROL_DUMMY": -0.0219
+  },
+  "stds": {
+    "CONSTANT": 1.3992,
+    "DELTA_LOG_ICE_BRENT_POSITIVE": -3.1713,
+    "DELTA_LOG_ICE_BRENT_NEGATIVE": -2.2304,
+    "LAGGED_INTEREST_DIFFERENTIAL": -1.9094,
+    "CHANGE_INTEREST_DIFFERENTIAL": -6.3554,
+    "CRISIS_CONTROL_DUMMY": -0.0219
+  },
+  "stats": {
+    "adjustedR2": 0.3339
   }
-  
+}
diff --git a/linreg.py b/linreg.py
@@ -6,6 +6,7 @@
 from sklearn.linear_model import LinearRegression
 from sklearn.model_selection import train_test_split
 
+
 class Columns(Enum):
     DATE = "Date"
     USDNOK = "TWI NKSP Index  (R1)"
@@ -14,29 +15,30 @@ class Columns(Enum):
 
 
 def regression(df: pd.DataFrame):
-
 
     X = df[[Columns.OIL.value, Columns.STOCKS.value]]
     Y = df[Columns.USDNOK.value]
-    X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.01,random_state=101)
+    X_train, X_test, Y_train, Y_test = train_test_split(
+        X, Y, test_size=0.01, random_state=101)
 
     lm = LinearRegression()
-    lm.fit(X_train,Y_train)
+    lm.fit(X_train, Y_train)
 
     print("STD", X_train.std(axis=0))
     print("coef.", lm.coef_)
-    print("norm. coef ", lm.coef_* X_train.std(axis=0))
-    return lm.coef_* X_train.std(axis=0)
+    print("norm. coef ", lm.coef_ * X_train.std(axis=0))
+    return lm.coef_ * X_train.std(axis=0)
 
     print(lm.coef_)
 
     prediction = lm.predict(X_test)
-    plt.scatter(Y_test,prediction)
+    plt.scatter(Y_test, prediction)
     plt.show()
 
+
 if __name__ == "__main__":
     # Load data from excel
-    df = pd.read_excel ("regression data.xlsx", sheet_name="2000-2022")
+    df = pd.read_excel("regression data.xlsx", sheet_name="2000-2022")
 
     # date_column = df[Columns.DATE.value]
 
@@ -60,19 +62,19 @@ def regression(df: pd.DataFrame):
     coefs = regression(df)
     print("coeffs full data: ", coefs)
 
-    # set to 1 means two year intervals, 2 is 3 etc. 
+    # set to 1 means two year intervals, 2 is 3 etc.
     interval = 5
 
-    #split data into years
+    # split data into years
     for i in range(2000, 2023, interval):
         # year_df = df[f"{i}-01-01":f"{i}-12-31"]
-        year_df = df.sort_index().loc[f"{i}-01-01":f"{i+(interval-1)}-12-31", :] 
+        year_df = df.sort_index(
+        ).loc[f"{i}-01-01":f"{i+(interval-1)}-12-31", :]
         coefs = regression(year_df)
         print(f"{i}-{i+(interval-1)}, {coefs[0]}, {coefs[1]}")
         oil.append(coefs[0])
         stocks.append(coefs[1])
         break
 
-    #print(oil)
-    #print(stocks)
-
+    # print(oil)
+    # print(stocks)