"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "notebooks/quick_start.ipynb" between
prophet-0.7.tar.gz and prophet-1.0.tar.gz

About: Prophet is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

quick_start.ipynb  (prophet-0.7):quick_start.ipynb  (prophet-1.0)
skipping to change at line 14 skipping to change at line 14
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
"metadata": { "metadata": {
"block_hidden": true "block_hidden": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"%load_ext rpy2.ipython\n", "%load_ext rpy2.ipython\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"import logging\n", "import logging\n",
"logging.getLogger('fbprophet').setLevel(logging.ERROR)\n", "logging.getLogger('prophet').setLevel(logging.ERROR)\n",
"import warnings\n", "import warnings\n",
"warnings.filterwarnings(\"ignore\")" "warnings.filterwarnings(\"ignore\")"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Python API\n", "## Python API\n",
"\n", "\n",
skipping to change at line 46 skipping to change at line 46
"First we'll import the data:" "First we'll import the data:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"import pandas as pd\n", "import pandas as pd\n",
"from fbprophet import Prophet" "from prophet import Prophet"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/html": [ "text/html": [
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"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 4,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_season "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
ality=True to override this.\n" "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonal
ity=True to override this.\n"
] ]
}, },
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<fbprophet.forecaster.Prophet at 0x7fa84319a9d0>" "<prophet.forecaster.Prophet at 0x7f5a64149790>"
] ]
}, },
"execution_count": 4, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"m = Prophet()\n", "m = Prophet()\n",
"m.fit(df)" "m.fit(df)"
skipping to change at line 288 skipping to change at line 289
" <th>ds</th>\n", " <th>ds</th>\n",
" <th>yhat</th>\n", " <th>yhat</th>\n",
" <th>yhat_lower</th>\n", " <th>yhat_lower</th>\n",
" <th>yhat_upper</th>\n", " <th>yhat_upper</th>\n",
" </tr>\n", " </tr>\n",
" </thead>\n", " </thead>\n",
" <tbody>\n", " <tbody>\n",
" <tr>\n", " <tr>\n",
" <th>3265</th>\n", " <th>3265</th>\n",
" <td>2017-01-15</td>\n", " <td>2017-01-15</td>\n",
" <td>8.204125</td>\n", " <td>8.211542</td>\n",
" <td>7.449654</td>\n", " <td>7.444742</td>\n",
" <td>8.946255</td>\n", " <td>8.903545</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>3266</th>\n", " <th>3266</th>\n",
" <td>2017-01-16</td>\n", " <td>2017-01-16</td>\n",
" <td>8.529148</td>\n", " <td>8.536553</td>\n",
" <td>7.792752</td>\n", " <td>7.847804</td>\n",
" <td>9.284594</td>\n", " <td>9.211145</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>3267</th>\n", " <th>3267</th>\n",
" <td>2017-01-17</td>\n", " <td>2017-01-17</td>\n",
" <td>8.316555</td>\n", " <td>8.323968</td>\n",
" <td>7.563541</td>\n", " <td>7.541829</td>\n",
" <td>9.029357</td>\n", " <td>9.035461</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>3268</th>\n", " <th>3268</th>\n",
" <td>2017-01-18</td>\n", " <td>2017-01-18</td>\n",
" <td>8.149153</td>\n", " <td>8.156621</td>\n",
" <td>7.384345</td>\n", " <td>7.404457</td>\n",
" <td>8.840279</td>\n", " <td>8.830642</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>3269</th>\n", " <th>3269</th>\n",
" <td>2017-01-19</td>\n", " <td>2017-01-19</td>\n",
" <td>8.161075</td>\n", " <td>8.168561</td>\n",
" <td>7.430352</td>\n", " <td>7.438865</td>\n",
" <td>8.859482</td>\n", " <td>8.908668</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
" ds yhat yhat_lower yhat_upper\n", " ds yhat yhat_lower yhat_upper\n",
"3265 2017-01-15 8.204125 7.449654 8.946255\n", "3265 2017-01-15 8.211542 7.444742 8.903545\n",
"3266 2017-01-16 8.529148 7.792752 9.284594\n", "3266 2017-01-16 8.536553 7.847804 9.211145\n",
"3267 2017-01-17 8.316555 7.563541 9.029357\n", "3267 2017-01-17 8.323968 7.541829 9.035461\n",
"3268 2017-01-18 8.149153 7.384345 8.840279\n", "3268 2017-01-18 8.156621 7.404457 8.830642\n",
"3269 2017-01-19 8.161075 7.430352 8.859482" "3269 2017-01-19 8.168561 7.438865 8.908668"
] ]
}, },
"execution_count": 6, "execution_count": 6,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"forecast = m.predict(future)\n", "forecast = m.predict(future)\n",
"forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()" "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()"
skipping to change at line 357 skipping to change at line 358
"You can plot the forecast by calling the `Prophet.plot` method and passing in your forecast dataframe." "You can plot the forecast by calling the `Prophet.plot` method and passing in your forecast dataframe."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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"text/plain": [ "text/plain": [
"<Figure size 720x432 with 1 Axes>" "<Figure size 720x432 with 1 Axes>"
] ]
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"fig1 = m.plot(forecast)" "fig1 = m.plot(forecast)"
skipping to change at line 384 skipping to change at line 385
"If you want to see the forecast components, you can use the `Prophet.plot_c omponents` method. By default you'll see the trend, yearly seasonality, and wee kly seasonality of the time series. If you include holidays, you'll see those h ere, too." "If you want to see the forecast components, you can use the `Prophet.plot_c omponents` method. By default you'll see the trend, yearly seasonality, and wee kly seasonality of the time series. If you include holidays, you'll see those h ere, too."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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"text/plain": [ "text/plain": [
"<Figure size 648x648 with 3 Axes>" "<Figure size 648x648 with 3 Axes>"
] ]
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"fig2 = m.plot_components(forecast)" "fig2 = m.plot_components(forecast)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"An interactive figure of the forecast and components can be created with pl otly. You will need to install plotly 4.0 or above separately, as it will not by default be installed with fbprophet. You will also need to install the `noteboo k` and `ipywidgets` packages." "An interactive figure of the forecast and components can be created with pl otly. You will need to install plotly 4.0 or above separately, as it will not by default be installed with prophet. You will also need to install the `notebook` and `ipywidgets` packages."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from fbprophet.plot import plot_plotly, plot_components_plotly\n", "from prophet.plot import plot_plotly, plot_components_plotly\n",
"\n", "\n",
"plot_plotly(m, forecast)" "plot_plotly(m, forecast)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
skipping to change at line 442 skipping to change at line 443
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## R API\n", "## R API\n",
"\n", "\n",
"In R, we use the normal model fitting API. We provide a `prophet` function that performs fitting and returns a model object. You can then call `predict` and `plot` on this model object." "In R, we use the normal model fitting API. We provide a `prophet` function that performs fitting and returns a model object. You can then call `predict` and `plot` on this model object."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 2,
"metadata": { "metadata": {},
"collapsed": true "outputs": [
}, {
"outputs": [], "name": "stderr",
"output_type": "stream",
"text": [
"R[write to console]: Loading required package: Rcpp\n",
"\n",
"R[write to console]: Loading required package: rlang\n",
"\n"
]
}
],
"source": [ "source": [
"%%R\n", "%%R\n",
"library(prophet)" "library(prophet)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and num eric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY- MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyton Manning's Wikipedia page, available [here](https://github.com/facebook/p rophet/blob/master/examples/example_wp_log_peyton_manning.csv)." "First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and num eric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY- MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyton Manning's Wikipedia page, available [here](https://github.com/facebook/p rophet/blob/master/examples/example_wp_log_peyton_manning.csv)."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 3,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"%%R\n", "%%R\n",
"df <- read.csv('../examples/example_wp_log_peyton_manning.csv')" "df <- read.csv('../examples/example_wp_log_peyton_manning.csv')"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We call the `prophet` function to fit the model. The first argument is the historical dataframe. Additional arguments control how Prophet fits the data a nd are described in later pages of this documentation." "We call the `prophet` function to fit the model. The first argument is the historical dataframe. Additional arguments control how Prophet fits the data a nd are described in later pages of this documentation."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 4,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "name": "stderr",
"text/plain": [ "output_type": "stream",
"Initial log joint probability = -19.4685\n", "text": [
"Optimization terminated normally: \n", "R[write to console]: Disabling daily seasonality. Run prophet with daily.
" Convergence detected: relative gradient magnitude is below tolerance\n seasonality=TRUE to override this.\n",
" "\n"
] ]
},
"metadata": {},
"output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"m <- prophet(df)" "m <- prophet(df)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Predictions are made on a dataframe with a column `ds` containing the dates for which predictions are to be made. The `make_future_dataframe` function take s the model object and a number of periods to forecast and produces a suitable d ataframe. By default it will also include the historical dates so we can evaluat e in-sample fit." "Predictions are made on a dataframe with a column `ds` containing the dates for which predictions are to be made. The `make_future_dataframe` function take s the model object and a number of periods to forecast and produces a suitable d ataframe. By default it will also include the historical dates so we can evaluat e in-sample fit."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "name": "stdout",
"text/plain": [ "output_type": "stream",
" ds\n", "text": [
"3265 2017-01-14\n", " ds\n",
"3266 2017-01-15\n", "3265 2017-01-14\n",
"3267 2017-01-16\n", "3266 2017-01-15\n",
"3268 2017-01-17\n", "3267 2017-01-16\n",
"3269 2017-01-18\n", "3268 2017-01-17\n",
"3270 2017-01-19\n" "3269 2017-01-18\n",
] "3270 2017-01-19\n"
}, ]
"metadata": {},
"output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"future <- make_future_dataframe(m, periods = 365)\n", "future <- make_future_dataframe(m, periods = 365)\n",
"tail(future)" "tail(future)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"As with most modeling procedures in R, we use the generic `predict` functio n to get our forecast. The `forecast` object is a dataframe with a column `yhat` containing the forecast. It has additional columns for uncertainty intervals an d seasonal components." "As with most modeling procedures in R, we use the generic `predict` functio n to get our forecast. The `forecast` object is a dataframe with a column `yhat` containing the forecast. It has additional columns for uncertainty intervals an d seasonal components."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "name": "stdout",
"text/plain": [ "output_type": "stream",
" ds yhat yhat_lower yhat_upper\n", "text": [
"3265 2017-01-14 7.824163 7.127881 8.609668\n", " ds yhat yhat_lower yhat_upper\n",
"3266 2017-01-15 8.205942 7.452071 8.904387\n", "3265 2017-01-14 7.818359 7.071228 8.550957\n",
"3267 2017-01-16 8.530942 7.742400 9.300974\n", "3266 2017-01-15 8.200125 7.475725 8.869495\n",
"3268 2017-01-17 8.318327 7.606534 9.071184\n", "3267 2017-01-16 8.525104 7.747071 9.226915\n",
"3269 2017-01-18 8.150948 7.440224 8.902922\n", "3268 2017-01-17 8.312482 7.551904 9.046774\n",
"3270 2017-01-19 8.162839 7.385953 8.890669\n" "3269 2017-01-18 8.145098 7.390770 8.863692\n",
] "3270 2017-01-19 8.156964 7.381716 8.866507\n"
}, ]
"metadata": {},
"output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"forecast <- predict(m, future)\n", "forecast <- predict(m, future)\n",
"tail(forecast[c('ds', 'yhat', 'yhat_lower', 'yhat_upper')])" "tail(forecast[c('ds', 'yhat', 'yhat_lower', 'yhat_upper')])"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"You can use the generic `plot` function to plot the forecast, by passing in the model and the forecast dataframe." "You can use the generic `plot` function to plot the forecast, by passing in the model and the forecast dataframe."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R -w 10 -h 6 -u in\n", "%%R -w 10 -h 6 -u in\n",
"plot(m, forecast)" "plot(m, forecast)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"You can use the `prophet_plot_components` function to see the forecast brok en down into trend, weekly seasonality, and yearly seasonality." "You can use the `prophet_plot_components` function to see the forecast brok en down into trend, weekly seasonality, and yearly seasonality."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R -w 9 -h 9 -u in\n", "%%R -w 9 -h 9 -u in\n",
"prophet_plot_components(m, forecast)" "prophet_plot_components(m, forecast)"
] ]
}, },
skipping to change at line 646 skipping to change at line 647
"language_info": { "language_info": {
"codemirror_mode": { "codemirror_mode": {
"name": "ipython", "name": "ipython",
"version": 3 "version": 3
}, },
"file_extension": ".py", "file_extension": ".py",
"mimetype": "text/x-python", "mimetype": "text/x-python",
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.8" "version": "3.8.3"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 1 "nbformat_minor": 1
} }
 End of changes. 27 change blocks. 
82 lines changed or deleted 83 lines changed or added

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