updated raw python

notebooks/Classify Data Example-kNN.ipynb

@@ -20,7 +20,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import turicreate as turi"
@@ -185,7 +187,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "userinput = turi.SFrame({\n",

notebooks/Classify Image Example-ResNet50.ipynb

@@ -38,7 +38,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "url = \"data/food_images\""
@@ -130,8 +132,8 @@
    ],
    "source": [
     "data = turi.image_analysis.load_images(url)\n",
-    "data['foodType'] = data['path'].apply(lambda path: 'Eggs' if 'eggs' in path else 'Soup')\n",
-    "data.save('egg_or_soup.sframe')\n",
+    "data[\"foodType\"] = data[\"path\"].apply(lambda path: \"Eggs\" if \"eggs\" in path else \"Soup\")\n",
+    "data.save(\"egg_or_soup.sframe\")\n",
     "data.explore()"
    ]
   },

raw_python/classify_data_knn.py

@@ -0,0 +1,33 @@
+# Import Turi Create package
+import turicreate as turi
+
+# Create an SFrame object.
+'''
+    Each column in the `SFrame` is an `SArray` object.
+    
+    In the following `SFrame` object, we have created a data buffer (dataset) that specifies the size of each pet's eyes, nose, and head.
+    
+    Each number in the dataset is a ratio from 15cm.
+    
+    Note:- The ratios in the dataset are NOT accurate or related to reality. Those are random ratios used for demonstration purposes.
+'''
+dataBuffer = turi.SFrame({
+                         "pet_types": ["cat", "dog", "wolf", "cat", "wolf", "dog"],
+                         "eyes":      [0.23,  0.64,  0.89,   0.26,  0.93,   0.66 ],
+                         "nose":      [0.11,  0.68,  0.78,   0.08,  0.74,   0.57 ],
+                         "head":      [0.34,  0.47,  0.66,   0.37,  0.68,   0.45 ]
+                         })
+
+# Create a nearest neighbor classifier using the SFrame object above.
+classifier = turi.nearest_neighbor_classifier.create(dataBuffer, target="pet_types")
+
+# Edit the input below. Make sure the numbers are between 0 and 1.
+userinput = turi.SFrame({
+                        "eyes":      [0.32],
+                        "nose":      [0.20],
+                        "head":      [0.42]
+                        })
+
+# Predict the input using the nearest neighbor classifier.
+result = classifier.predict(userinput, max_neighbors=2)
+print result

raw_python/classify_image_resnet.py

@@ -0,0 +1,37 @@
+# Import Turi Create package
+import turicreate as turi
+
+# Refrence the dataset path
+url = "/Users/ahmedbekhit/Documents/Data/Development/TuriCreate/repo/turicreate-notebook/notebooks/data/food_images"
+
+# Label the dataset
+## Load the dataset folder image content using the image_analysis property
+data = turi.image_analysis.load_images(url)
+## Create a "foodType" key for each image in the dataset to specify whether it's an Egg or Soup, based on which folder it's located in.
+data["foodType"] = data["path"].apply(lambda path: "Eggs" if "eggs" in path else "Soup")
+## Export the labeled images as an SFrame object in order to use it while creating our image classifier.
+data.save("egg_or_soup.sframe")
+## Visualize the new labeled images list.
+data.explore()
+
+# Load the SFrame object that contains the labeled images.
+dataBuffer = turi.SFrame("egg_or_soup.sframe")
+
+# Randmly split the SFrame object
+'''
+    90% of the data from the original SFrame object will be used to train the image classifier.
+    10% of the data from the original SFrame object will be used to test the image classifier.
+'''
+trainingBuffers, testingBuffers = dataBuffer.random_split(0.9)
+
+# Train the image classifier using the Residual Network architecture and pre-trained model.
+model = turi.image_classifier.create(trainingBuffers, target="foodType", model="resnet-50")
+
+# Evaluate the test data to determine the model accuracy
+evaluations = model.evaluate(testingBuffers)
+
+# Save the Turi Create model to retrieve it later
+model.save("egg_or_soup.model")
+
+# Export our new trained image classification model for Core ML.
+model.export_coreml("FoodClassifier.mlmodel")

raw_python/create_data.py

@@ -0,0 +1,19 @@
+# Import Turi Create package
+import turicreate as turi
+
+# Create an SFrame object.
+'''
+    Each column in the `SFrame` is an `SArray` object.
+    
+    In the following `SFrame` object, we have created a data buffer (dataset) that specifies the size of each pet's eyes, nose, and head.
+    
+    Each number in the dataset is a ratio from 15cm.
+    
+    Note:- The ratios in the dataset are NOT accurate or related to reality. Those are random ratios used for demonstration purposes.
+    '''
+dataBuffer = turi.SFrame({
+                         "pet_types": ["cat", "dog", "wolf", "cat", "wolf", "dog"],
+                         "eyes":      [0.23,  0.64,  0.89,   0.26,  0.93,   0.66 ],
+                         "nose":      [0.11,  0.68,  0.78,   0.08,  0.74,   0.57 ],
+                         "head":      [0.34,  0.47,  0.66,   0.37,  0.68,   0.45 ]
+                         })

raw_python/sample_CSV_reading.py

@@ -0,0 +1,16 @@
+# Import Turi Create package
+import turicreate as turi
+# Reads the data file, in this case we will read data from a CSV file
+'''
+    Each column in the `SFrame` is an `SArray` object.
+    
+    In the following line of code, the parameter `header` is set to `False` because the **CSV** data file we're using doesn't have a header.
+    
+    Note:- The `header` parameter is `True` by default and if we didn't set it to `False` it would show us the first **n+1** rows since it counts the first as a header automatically.
+'''
+dataBuffer = turi.SFrame.read_csv('/Users/ahmedbekhit/Documents/Data/Development/TuriCreate/SampleCSVFile_119kb.csv', header=False)
+
+# Load the first 5 rows.
+dataBuffer.head(5)
+
+print dataBuffer