Merge pull request #39 from jGaboardi/01_blacken_repo

Format repo with `black`

Author: jGaboardi

notebooks/Binomial_GLM.ipynb

@@ -18,8 +18,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Load sample dataset - Subset of london house price dataset\n",
-    "db = ps.open(ps.get_path('columbus.dbf'),'r')\n",
+    "# Load sample dataset - Subset of london house price dataset\n",
+    "db = ps.open(ps.get_path(\"columbus.dbf\"), \"r\")\n",
     "\n",
     "#Set dependent variable\n",
     "y = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
@@ -73,12 +73,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Estimate Binomial GLM\n",
+    "# Estimate Binomial GLM\n",
     "\n",
-    "#First instantiate a GLM model object\n",
-    "model = GLM(y, X, family=Binomial()) #Set family to Binomial family object for Binomial GLM\n",
+    "# First instantiate a GLM model object\n",
+    "model = GLM(\n",
+    "    y, X, family=Binomial()\n",
+    ")  # Set family to Binomial family object for Binomial GLM\n",
     "\n",
-    "#Then use the fit method to estimate coefficients and compute diagnostics\n",
+    "# Then use the fit method to estimate coefficients and compute diagnostics\n",
     "results = model.fit()"
    ]
   },
@@ -96,7 +98,7 @@
     }
    ],
    "source": [
-    "#Estimated prameters, intercept is always the first column on the left\n",
+    "# Estimated prameters, intercept is always the first column on the left\n",
     "print(results.params)"
    ]
   },
@@ -114,7 +116,7 @@
     }
    ],
    "source": [
-    "#Parameter standard errors\n",
+    "# Parameter standard errors\n",
     "print(results.bse)"
    ]
   },
@@ -132,7 +134,7 @@
     }
    ],
    "source": [
-    "#Parameter t-values\n",
+    "# Parameter t-values\n",
     "print(results.tvalues)"
    ]
   },
@@ -150,7 +152,7 @@
     }
    ],
    "source": [
-    "#Model AIC\n",
+    "# Model AIC\n",
     "print(results.aic)"
    ]
   }
@@ -176,5 +178,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

notebooks/Gaussian_GLM.ipynb

@@ -17,14 +17,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Load sample dataset - columbus dataset \n",
-    "db = ps.open(ps.get_path('columbus.dbf'),'r')\n",
+    "# Load sample dataset - columbus dataset\n",
+    "db = ps.open(ps.get_path(\"columbus.dbf\"), \"r\")\n",
     "\n",
-    "#Set dependent variable\n",
+    "# Set dependent variable\n",
     "y = np.array(db.by_col(\"HOVAL\"))\n",
-    "y = np.reshape(y, (49,1))\n",
+    "y = np.reshape(y, (49, 1))\n",
     "\n",
-    "#Set indepdent varibLES\n",
+    "# Set indepdent varibLES\n",
     "X = []\n",
     "X.append(db.by_col(\"INC\"))\n",
     "X.append(db.by_col(\"CRIME\"))\n",
@@ -37,12 +37,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Estimate Gaussian GLM\n",
+    "# Estimate Gaussian GLM\n",
     "\n",
-    "#First instantiate a GLM model object\n",
-    "model = GLM(y, X) #Gaussian is the default family parameter so it doesn't need to be set\n",
+    "# First instantiate a GLM model object\n",
+    "model = GLM(\n",
+    "    y, X\n",
+    ")  # Gaussian is the default family parameter so it doesn't need to be set\n",
     "\n",
-    "#Then use the fit method to estimate coefficients and compute diagnostics\n",
+    "# Then use the fit method to estimate coefficients and compute diagnostics\n",
     "results = model.fit()"
    ]
   },
@@ -60,7 +62,7 @@
     }
    ],
    "source": [
-    "#Estimated prameters, intercept is always the first column on the left\n",
+    "# Estimated prameters, intercept is always the first column on the left\n",
     "print(results.params)"
    ]
   },
@@ -78,7 +80,7 @@
     }
    ],
    "source": [
-    "#Parameter standard errors\n",
+    "# Parameter standard errors\n",
     "print(results.bse)"
    ]
   },
@@ -96,7 +98,7 @@
     }
    ],
    "source": [
-    "#Parameter t-values\n",
+    "# Parameter t-values\n",
     "print(results.tvalues)"
    ]
   },
@@ -114,7 +116,7 @@
     }
    ],
    "source": [
-    "#Model AIC\n",
+    "# Model AIC\n",
     "print(results.aic)"
    ]
   }

notebooks/Poisson_GLM.ipynb

@@ -18,16 +18,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Load sample dataset - columbus dataset \n",
-    "db = ps.open(ps.get_path('columbus.dbf'),'r')\n",
+    "# Load sample dataset - columbus dataset\n",
+    "db = ps.open(ps.get_path(\"columbus.dbf\"), \"r\")\n",
     "\n",
-    "#Set dependent variable\n",
+    "# Set dependent variable\n",
     "y = np.array(db.by_col(\"HOVAL\"))\n",
-    "y = np.reshape(y, (49,1))\n",
-    "#Round dependent variable and convert to integer for the example since Poisson is for discrete data\n",
+    "y = np.reshape(y, (49, 1))\n",
+    "# Round dependent variable and convert to integer for the example since Poisson is for discrete data\n",
     "y = np.round(y).astype(int)\n",
     "\n",
-    "#Set indepdent varibLES\n",
+    "# Set indepdent varibLES\n",
     "X = []\n",
     "X.append(db.by_col(\"INC\"))\n",
     "X.append(db.by_col(\"CRIME\"))\n",
@@ -40,12 +40,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Estimate Poisson GLM\n",
+    "# Estimate Poisson GLM\n",
     "\n",
-    "#First instantiate a GLM model object\n",
-    "model = GLM(y, X, family=Poisson()) #Set family to Poisson family object for Poisson GLM\n",
+    "# First instantiate a GLM model object\n",
+    "model = GLM(\n",
+    "    y, X, family=Poisson()\n",
+    ")  # Set family to Poisson family object for Poisson GLM\n",
     "\n",
-    "#Then use the fit method to estimate coefficients and compute diagnostics\n",
+    "# Then use the fit method to estimate coefficients and compute diagnostics\n",
     "results = model.fit()"
    ]
   },
@@ -63,7 +65,7 @@
     }
    ],
    "source": [
-    "#Estimated prameters, intercept is always the first column on the left\n",
+    "# Estimated prameters, intercept is always the first column on the left\n",
     "print(results.params)"
    ]
   },
@@ -81,7 +83,7 @@
     }
    ],
    "source": [
-    "#Parameter standard errors\n",
+    "# Parameter standard errors\n",
     "print(results.bse)"
    ]
   },
@@ -99,7 +101,7 @@
     }
    ],
    "source": [
-    "#Parameter t-values\n",
+    "# Parameter t-values\n",
     "print(results.tvalues)"
    ]
   },
@@ -117,7 +119,7 @@
     }
    ],
    "source": [
-    "#Model AIC\n",
+    "# Model AIC\n",
     "print(results.aic)"
    ]
   }

spglm/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '1.0.8'
+__version__ = "1.0.8"
 
 from . import glm
 from . import family