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fixing errors (location, legend, uri) #41

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25 changes: 13 additions & 12 deletions ipython-notebook/API Sample/Batch anomaly detection with the Anomaly Detector API.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -76,7 +76,8 @@
"source": [
"def detect(endpoint, apikey, request_data):\n",
" headers = {'Content-Type': 'application/json', 'Ocp-Apim-Subscription-Key': apikey}\n",
" response = requests.post(endpoint, data=json.dumps(request_data), headers=headers)\n",
" endpoint_uri = f\"https://{endpoint}\"\n",
" response = requests.post(endpoint_uri, data=json.dumps(request_data), headers=headers)\n",
" if response.status_code == 200:\n",
" return json.loads(response.content.decode(\"utf-8\"))\n",
" else:\n",
Expand All @@ -92,9 +93,9 @@
"source": [
"def build_figure(sample_data, sensitivity):\n",
" sample_data['sensitivity'] = sensitivity\n",
" result = detect(endpoint, apikey, sample_data)\n",
" columns = {'expectedValues': result['expectedValues'], 'isAnomaly': result['isAnomaly'], 'isNegativeAnomaly': result['isNegativeAnomaly'],\n",
" 'isPositiveAnomaly': result['isPositiveAnomaly'], 'upperMargins': result['upperMargins'], 'lowerMargins': result['lowerMargins'],\n",
" result = detect(endpoint_latest, apikey, sample_data)\n",
" columns = {'expectedValue': result['expectedValue'], 'isAnomaly': result['isAnomaly'], 'isNegativeAnomaly': result['isNegativeAnomaly'],\n",
" 'isPositiveAnomaly': result['isPositiveAnomaly'], 'upperMargin': result['upperMargin'], 'lowerMargin': result['lowerMargin'],\n",
" 'timestamp': [parser.parse(x['timestamp']) for x in sample_data['series']], \n",
" 'value': [x['value'] for x in sample_data['series']]}\n",
" response = pd.DataFrame(data=columns)\n",
Expand All @@ -106,19 +107,19 @@
" anomaly_indexes = []\n",
" p = figure(x_axis_type='datetime', title=\"Batch Anomaly Detection ({0} Sensitvity)\".format(sensitivity), width=800, height=600)\n",
" for anom in response['isAnomaly']:\n",
" if anom == True and (values[index] > response.iloc[index]['expectedValues'] + response.iloc[index]['upperMargins'] or \n",
" values[index] < response.iloc[index]['expectedValues'] - response.iloc[index]['lowerMargins']):\n",
" if anom == True and (values[index] > response.iloc[index]['expectedValue'] + response.iloc[index]['upperMargin'] or \n",
" values[index] < response.iloc[index]['expectedValue'] - response.iloc[index]['lowerMargin']):\n",
" anomalies.append(values[index])\n",
" anomaly_labels.append(label[index])\n",
" anomaly_indexes.append(index)\n",
" index = index+1\n",
" upperband = response['expectedValues'] + response['upperMargins']\n",
" lowerband = response['expectedValues'] -response['lowerMargins']\n",
" upperband = response['expectedValue'] + response['upperMargin']\n",
" lowerband = response['expectedValue'] -response['lowerMargin']\n",
" band_x = np.append(label, label[::-1])\n",
" band_y = np.append(lowerband, upperband[::-1])\n",
" boundary = p.patch(band_x, band_y, color=Blues4[2], fill_alpha=0.5, line_width=1, legend='Boundary')\n",
" p.line(label, values, legend='Value', color=\"#2222aa\", line_width=1)\n",
" p.line(label, response['expectedValues'], legend='ExpectedValue', line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n",
" p.line(label, response['expectedValue'], legend='ExpectedValue', line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n",
" anom_source = ColumnDataSource(dict(x=anomaly_labels, y=anomalies))\n",
" anoms = p.circle('x', 'y', size=5, color='tomato', source=anom_source)\n",
" p.legend.border_line_width = 1\n",
Expand Down Expand Up @@ -149,7 +150,7 @@
"outputs": [],
"source": [
"# Hourly Sample\n",
"sample_data = json.load(open('univariate_sample_hourly.json'))\n",
"sample_data = json.load(open('../SDK Sample/univariate_sample_hourly.json'))\n",
"sample_data['granularity'] = 'hourly'\n",
"sample_data['period'] = 24\n",
"# 95 sensitivity\n",
Expand Down Expand Up @@ -190,7 +191,7 @@
"outputs": [],
"source": [
"#daily sample\n",
"sample_data = json.load(open('univariate_sample_daily.json'))\n",
"sample_data = json.load(open('../SDK Sample/univariate_sample_daily.json'))\n",
"sample_data['granularity'] = 'daily'\n",
"# 95 sensitivity\n",
"build_figure(sample_data,95)"
Expand Down Expand Up @@ -238,4 +239,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}
13 changes: 7 additions & 6 deletions ipython-notebook/API Sample/Latest point detection with the Anomaly Detector API.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,8 @@
"source": [
"def detect(endpoint, apikey, request_data):\n",
" headers = {'Content-Type': 'application/json', 'Ocp-Apim-Subscription-Key': apikey}\n",
" response = requests.post(endpoint, data=json.dumps(request_data), headers=headers)\n",
" endpoint_uri = f\"https://{endpoint}\"\n",
" response = requests.post(endpoint_uri, data=json.dumps(request_data), headers=headers)\n",
" if response.status_code == 200:\n",
" return json.loads(response.content.decode(\"utf-8\"))\n",
" else:\n",
Expand Down Expand Up @@ -117,9 +118,9 @@
" lowerband = response['expectedValues'] -response['lowerMargins']\n",
" band_x = np.append(label, label[::-1])\n",
" band_y = np.append(lowerband, upperband[::-1])\n",
" boundary = p.patch(band_x, band_y, color=Blues4[2], fill_alpha=0.5, line_width=1, legend='Boundary')\n",
" p.line(label, values, legend='value', color=\"#2222aa\", line_width=1)\n",
" p.line(label, response['expectedValues'], legend='expectedValue', line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n",
" boundary = p.patch(band_x, band_y, color=Blues4[2], fill_alpha=0.5, line_width=1, legend_label='Boundary')\n",
" p.line(label, values, legend_label='value', color=\"#2222aa\", line_width=1)\n",
" p.line(label, response['expectedValues'], legend_label='expectedValue', line_width=1, line_dash=\"dotdash\", line_color='olivedrab')\n",
" anom_source = ColumnDataSource(dict(x=anomaly_labels, y=anomalies))\n",
" anoms = p.circle('x', 'y', size=5, color='tomato', source=anom_source)\n",
" p.legend.border_line_width = 1\n",
Expand Down Expand Up @@ -148,7 +149,7 @@
"outputs": [],
"source": [
"def detect_anomaly(sensitivity):\n",
" sample_data = json.load(open('univariate_sample_daily.json'))\n",
" sample_data = json.load(open('../SDK Sample/univariate_sample_daily.json'))\n",
" points = sample_data['series']\n",
" skip_point = 29\n",
" result = {'expectedValues': [None]*len(points), 'upperMargins': [None]*len(points), \n",
Expand Down Expand Up @@ -217,4 +218,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}